{
    "file_diffs": [
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            "old_file_content": "from __future__ import annotations\nimport math\n\nimport pytest\n\nfrom PIL import Image, ImageTransform\n\nfrom .helper import assert_image_equal, assert_image_similar, hopper\n\n\nclass TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)\n\n\nclass TestImageTransformAffine:\n    transform = Image.Transform.AFFINE\n\n    def _test_image(self):\n        im = hopper(\"RGB\")\n        return im.crop((10, 20, im.width - 10, im.height - 20))\n\n    @pytest.mark.parametrize(\n        \"deg, transpose\",\n        (\n            (0, None),\n            (90, Image.Transpose.ROTATE_90),\n            (180, Image.Transpose.ROTATE_180),\n            (270, Image.Transpose.ROTATE_270),\n        ),\n    )\n    def test_rotate(self, deg, transpose):\n        im = self._test_image()\n\n        angle = -math.radians(deg)\n        matrix = [\n            round(math.cos(angle), 15),\n            round(math.sin(angle), 15),\n            0.0,\n            round(-math.sin(angle), 15),\n            round(math.cos(angle), 15),\n            0.0,\n            0,\n            0,\n        ]\n        matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2\n        matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2\n\n        if transpose is not None:\n            transposed = im.transpose(transpose)\n        else:\n            transposed = im\n\n        for resample in [\n            Image.Resampling.NEAREST,\n            Image.Resampling.BILINEAR,\n            Image.Resampling.BICUBIC,\n        ]:\n            transformed = im.transform(\n                transposed.size, self.transform, matrix, resample\n            )\n            assert_image_equal(transposed, transformed)\n\n    @pytest.mark.parametrize(\n        \"scale, epsilon_scale\",\n        (\n            (1.1, 6.9),\n            (1.5, 5.5),\n            (2.0, 5.5),\n            (2.3, 3.7),\n            (2.5, 3.7),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample,epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 2),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_resize(self, scale, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width * scale)), int(round(im.height * scale))\n        matrix_up = [1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0]\n        matrix_down = [scale, 0, 0, 0, scale, 0, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n    @pytest.mark.parametrize(\n        \"x, y, epsilon_scale\",\n        (\n            (0.1, 0, 3.7),\n            (0.6, 0, 9.1),\n            (50, 50, 0),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample, epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 1.5),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_translate(self, x, y, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width + x)), int(round(im.height + y))\n        matrix_up = [1, 0, -x, 0, 1, -y, 0, 0]\n        matrix_down = [1, 0, x, 0, 1, y, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n\nclass TestImageTransformPerspective(TestImageTransformAffine):\n    # Repeat all tests for AFFINE transformations with PERSPECTIVE\n    transform = Image.Transform.PERSPECTIVE\n",
            "new_file_content": "from __future__ import annotations\nimport math\n\nimport pytest\n\nfrom PIL import Image, ImageTransform\n\nfrom .helper import assert_image_equal, assert_image_similar, hopper\n\n\nclass TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.PerspectiveTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)\n\n\nclass TestImageTransformAffine:\n    transform = Image.Transform.AFFINE\n\n    def _test_image(self):\n        im = hopper(\"RGB\")\n        return im.crop((10, 20, im.width - 10, im.height - 20))\n\n    @pytest.mark.parametrize(\n        \"deg, transpose\",\n        (\n            (0, None),\n            (90, Image.Transpose.ROTATE_90),\n            (180, Image.Transpose.ROTATE_180),\n            (270, Image.Transpose.ROTATE_270),\n        ),\n    )\n    def test_rotate(self, deg, transpose):\n        im = self._test_image()\n\n        angle = -math.radians(deg)\n        matrix = [\n            round(math.cos(angle), 15),\n            round(math.sin(angle), 15),\n            0.0,\n            round(-math.sin(angle), 15),\n            round(math.cos(angle), 15),\n            0.0,\n            0,\n            0,\n        ]\n        matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2\n        matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2\n\n        if transpose is not None:\n            transposed = im.transpose(transpose)\n        else:\n            transposed = im\n\n        for resample in [\n            Image.Resampling.NEAREST,\n            Image.Resampling.BILINEAR,\n            Image.Resampling.BICUBIC,\n        ]:\n            transformed = im.transform(\n                transposed.size, self.transform, matrix, resample\n            )\n            assert_image_equal(transposed, transformed)\n\n    @pytest.mark.parametrize(\n        \"scale, epsilon_scale\",\n        (\n            (1.1, 6.9),\n            (1.5, 5.5),\n            (2.0, 5.5),\n            (2.3, 3.7),\n            (2.5, 3.7),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample,epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 2),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_resize(self, scale, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width * scale)), int(round(im.height * scale))\n        matrix_up = [1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0]\n        matrix_down = [scale, 0, 0, 0, scale, 0, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n    @pytest.mark.parametrize(\n        \"x, y, epsilon_scale\",\n        (\n            (0.1, 0, 3.7),\n            (0.6, 0, 9.1),\n            (50, 50, 0),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample, epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 1.5),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_translate(self, x, y, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width + x)), int(round(im.height + y))\n        matrix_up = [1, 0, -x, 0, 1, -y, 0, 0]\n        matrix_down = [1, 0, x, 0, 1, y, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n\nclass TestImageTransformPerspective(TestImageTransformAffine):\n    # Repeat all tests for AFFINE transformations with PERSPECTIVE\n    transform = Image.Transform.PERSPECTIVE\n",
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                            },
                            {
                                "content": "        transform = ImageTransform.AffineTransform(seq[:6])",
                                "type": "context"
                            },
                            {
                                "content": "        im.transform((100, 100), transform)",
                                "type": "context"
                            },
                            {
                                "content": "        transform = ImageTransform.PerspectiveTransform(seq[:8])",
                                "type": "added"
                            },
                            {
                                "content": "        im.transform((100, 100), transform)",
                                "type": "added"
                            },
                            {
                                "content": "        transform = ImageTransform.ExtentTransform(seq[:4])",
                                "type": "context"
                            },
                            {
                                "content": "        im.transform((100, 100), transform)",
                                "type": "context"
                            },
                            {
                                "content": "        transform = ImageTransform.QuadTransform(seq[:8])",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Tests/test_image_transform.py",
                            "type": "method",
                            "name": "TestImageTransform.test_sanity",
                            "content": "class TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.PerspectiveTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 26,
                            "parent": {
                                "file_name": "Tests/test_image_transform.py",
                                "type": "class",
                                "name": "TestImageTransform",
                                "content": "class TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.PerspectiveTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 241,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Tests/test_image_transform.py",
                            "type": "class",
                            "name": "TestImageTransform",
                            "content": "class TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.PerspectiveTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)",
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            "old_file_content": "\n.. py:module:: PIL.ImageTransform\n.. py:currentmodule:: PIL.ImageTransform\n\n:py:mod:`~PIL.ImageTransform` Module\n====================================\n\nThe :py:mod:`~PIL.ImageTransform` module contains implementations of\n:py:class:`~PIL.Image.ImageTransformHandler` for some of the builtin\n:py:class:`.Image.Transform` methods.\n\n.. autoclass:: Transform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: AffineTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: ExtentTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: QuadTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: MeshTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n",
            "new_file_content": "\n.. py:module:: PIL.ImageTransform\n.. py:currentmodule:: PIL.ImageTransform\n\n:py:mod:`~PIL.ImageTransform` Module\n====================================\n\nThe :py:mod:`~PIL.ImageTransform` module contains implementations of\n:py:class:`~PIL.Image.ImageTransformHandler` for some of the builtin\n:py:class:`.Image.Transform` methods.\n\n.. autoclass:: Transform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: AffineTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: PerspectiveTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: ExtentTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: QuadTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n.. autoclass:: MeshTransform\n    :members:\n    :undoc-members:\n    :show-inheritance:\n",
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                        "section": "The :py:mod:`~PIL.ImageTransform` module contains implementations of"
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            "old_file_content": "#\n# The Python Imaging Library.\n# $Id$\n#\n# transform wrappers\n#\n# History:\n# 2002-04-08 fl   Created\n#\n# Copyright (c) 2002 by Secret Labs AB\n# Copyright (c) 2002 by Fredrik Lundh\n#\n# See the README file for information on usage and redistribution.\n#\nfrom __future__ import annotations\n\nfrom typing import Sequence\n\nfrom . import Image\n\n\nclass Transform(Image.ImageTransformHandler):\n    \"\"\"Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`.\"\"\"\n\n    method: Image.Transform\n\n    def __init__(self, data: Sequence[int]) -> None:\n        self.data = data\n\n    def getdata(self) -> tuple[Image.Transform, Sequence[int]]:\n        return self.method, self.data\n\n    def transform(\n        self,\n        size: tuple[int, int],\n        image: Image.Image,\n        **options: dict[str, str | int | tuple[int, ...] | list[int]],\n    ) -> Image.Image:\n        \"\"\"Perform the transform. Called from :py:meth:`.Image.transform`.\"\"\"\n        # can be overridden\n        method, data = self.getdata()\n        return image.transform(size, method, data, **options)\n\n\nclass AffineTransform(Transform):\n    \"\"\"\n    Define an affine image transform.\n\n    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first\n    two rows from an affine transform matrix. For each pixel (x, y) in the\n    output image, the new value is taken from a position (a x + b y + c,\n    d x + e y + f) in the input image, rounded to nearest pixel.\n\n    This function can be used to scale, translate, rotate, and shear the\n    original image.\n\n    See :py:meth:`.Image.transform`\n\n    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows\n        from an affine transform matrix.\n    \"\"\"\n\n    method = Image.Transform.AFFINE\n\n\nclass ExtentTransform(Transform):\n    \"\"\"\n    Define a transform to extract a subregion from an image.\n\n    Maps a rectangle (defined by two corners) from the image to a rectangle of\n    the given size. The resulting image will contain data sampled from between\n    the corners, such that (x0, y0) in the input image will end up at (0,0) in\n    the output image, and (x1, y1) at size.\n\n    This method can be used to crop, stretch, shrink, or mirror an arbitrary\n    rectangle in the current image. It is slightly slower than crop, but about\n    as fast as a corresponding resize operation.\n\n    See :py:meth:`.Image.transform`\n\n    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the\n        input image's coordinate system. See :ref:`coordinate-system`.\n    \"\"\"\n\n    method = Image.Transform.EXTENT\n\n\nclass QuadTransform(Transform):\n    \"\"\"\n    Define a quad image transform.\n\n    Maps a quadrilateral (a region defined by four corners) from the image to a\n    rectangle of the given size.\n\n    See :py:meth:`.Image.transform`\n\n    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the\n        upper left, lower left, lower right, and upper right corner of the\n        source quadrilateral.\n    \"\"\"\n\n    method = Image.Transform.QUAD\n\n\nclass MeshTransform(Transform):\n    \"\"\"\n    Define a mesh image transform.  A mesh transform consists of one or more\n    individual quad transforms.\n\n    See :py:meth:`.Image.transform`\n\n    :param data: A list of (bbox, quad) tuples.\n    \"\"\"\n\n    method = Image.Transform.MESH\n",
            "new_file_content": "#\n# The Python Imaging Library.\n# $Id$\n#\n# transform wrappers\n#\n# History:\n# 2002-04-08 fl   Created\n#\n# Copyright (c) 2002 by Secret Labs AB\n# Copyright (c) 2002 by Fredrik Lundh\n#\n# See the README file for information on usage and redistribution.\n#\nfrom __future__ import annotations\n\nfrom typing import Sequence\n\nfrom . import Image\n\n\nclass Transform(Image.ImageTransformHandler):\n    \"\"\"Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`.\"\"\"\n\n    method: Image.Transform\n\n    def __init__(self, data: Sequence[int]) -> None:\n        self.data = data\n\n    def getdata(self) -> tuple[Image.Transform, Sequence[int]]:\n        return self.method, self.data\n\n    def transform(\n        self,\n        size: tuple[int, int],\n        image: Image.Image,\n        **options: dict[str, str | int | tuple[int, ...] | list[int]],\n    ) -> Image.Image:\n        \"\"\"Perform the transform. Called from :py:meth:`.Image.transform`.\"\"\"\n        # can be overridden\n        method, data = self.getdata()\n        return image.transform(size, method, data, **options)\n\n\nclass AffineTransform(Transform):\n    \"\"\"\n    Define an affine image transform.\n\n    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first\n    two rows from an affine transform matrix. For each pixel (x, y) in the\n    output image, the new value is taken from a position (a x + b y + c,\n    d x + e y + f) in the input image, rounded to nearest pixel.\n\n    This function can be used to scale, translate, rotate, and shear the\n    original image.\n\n    See :py:meth:`.Image.transform`\n\n    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows\n        from an affine transform matrix.\n    \"\"\"\n\n    method = Image.Transform.AFFINE\n\n\nclass PerspectiveTransform(Transform):\n    \"\"\"\n    Define a perspective image transform.\n\n    This function takes an 8-tuple (a, b, c, d, e, f, g, h). For each pixel\n    (x, y) in the output image, the new value is taken from a position\n    ((a x + b y + c) / (g x + h y + 1), (d x + e y + f) / (g x + h y + 1)) in\n    the input image, rounded to nearest pixel.\n\n    This function can be used to scale, translate, rotate, and shear the\n    original image.\n\n    See :py:meth:`.Image.transform`\n\n    :param matrix: An 8-tuple (a, b, c, d, e, f, g, h).\n    \"\"\"\n\n    method = Image.Transform.PERSPECTIVE\n\n\nclass ExtentTransform(Transform):\n    \"\"\"\n    Define a transform to extract a subregion from an image.\n\n    Maps a rectangle (defined by two corners) from the image to a rectangle of\n    the given size. The resulting image will contain data sampled from between\n    the corners, such that (x0, y0) in the input image will end up at (0,0) in\n    the output image, and (x1, y1) at size.\n\n    This method can be used to crop, stretch, shrink, or mirror an arbitrary\n    rectangle in the current image. It is slightly slower than crop, but about\n    as fast as a corresponding resize operation.\n\n    See :py:meth:`.Image.transform`\n\n    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the\n        input image's coordinate system. See :ref:`coordinate-system`.\n    \"\"\"\n\n    method = Image.Transform.EXTENT\n\n\nclass QuadTransform(Transform):\n    \"\"\"\n    Define a quad image transform.\n\n    Maps a quadrilateral (a region defined by four corners) from the image to a\n    rectangle of the given size.\n\n    See :py:meth:`.Image.transform`\n\n    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the\n        upper left, lower left, lower right, and upper right corner of the\n        source quadrilateral.\n    \"\"\"\n\n    method = Image.Transform.QUAD\n\n\nclass MeshTransform(Transform):\n    \"\"\"\n    Define a mesh image transform.  A mesh transform consists of one or more\n    individual quad transforms.\n\n    See :py:meth:`.Image.transform`\n\n    :param data: A list of (bbox, quad) tuples.\n    \"\"\"\n\n    method = Image.Transform.MESH\n",
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                                "content": "    This function can be used to scale, translate, rotate, and shear the",
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                                "content": "    :param matrix: An 8-tuple (a, b, c, d, e, f, g, h).",
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    "old_commit_hash": "ba6399cad14d816cafc44c25782d6a3153082ae4^",
    "new_commit_hash": "ba6399cad14d816cafc44c25782d6a3153082ae4",
    "commit_message": "Added PerspectiveTransform",
    "commit_date": "2024-01-07T19:34:27+11:00",
    "metadata": {}
}
{
    "repo_name": "pillow",
    "new_commit_hash": "ba6399cad14d816cafc44c25782d6a3153082ae4",
    "test_file_codes": [
        "from __future__ import annotations\nimport math\n\nimport pytest\n\nfrom PIL import Image, ImageTransform\n\nfrom .helper import assert_image_equal, assert_image_similar, hopper\n\n\nclass TestImageTransform:\n    def test_sanity(self):\n        im = Image.new(\"L\", (100, 100))\n\n        seq = tuple(range(10))\n\n        transform = ImageTransform.AffineTransform(seq[:6])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.PerspectiveTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.ExtentTransform(seq[:4])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.QuadTransform(seq[:8])\n        im.transform((100, 100), transform)\n        transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])\n        im.transform((100, 100), transform)\n\n    def test_info(self):\n        comment = b\"File written by Adobe Photoshop\\xa8 4.0\"\n\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            assert im.info[\"comment\"] == comment\n\n            transform = ImageTransform.ExtentTransform((0, 0, 0, 0))\n            new_im = im.transform((100, 100), transform)\n        assert new_im.info[\"comment\"] == comment\n\n    def test_palette(self):\n        with Image.open(\"Tests/images/hopper.gif\") as im:\n            transformed = im.transform(\n                im.size, Image.Transform.AFFINE, [1, 0, 0, 0, 1, 0]\n            )\n            assert im.palette.palette == transformed.palette.palette\n\n    def test_extent(self):\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w // 2, h // 2),  # ul -> lr\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.resize((w * 2, h * 2), Image.Resampling.BILINEAR).crop((0, 0, w, h))\n\n        # undone -- precision?\n        assert_image_similar(transformed, scaled, 23)\n\n    def test_quad(self):\n        # one simple quad transform, equivalent to scale & crop upper left quad\n        im = hopper(\"RGB\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.QUAD,\n            (0, 0, 0, h // 2, w // 2, h // 2, w // 2, 0),  # ul -> ccw around quad\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w, h),\n            Image.Transform.AFFINE,\n            (0.5, 0, 0, 0, 0.5, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        assert_image_equal(transformed, scaled)\n\n    @pytest.mark.parametrize(\n        \"mode, expected_pixel\",\n        (\n            (\"RGB\", (255, 0, 0)),\n            (\"RGBA\", (255, 0, 0, 255)),\n            (\"LA\", (76, 0)),\n        ),\n    )\n    def test_fill(self, mode, expected_pixel):\n        im = hopper(mode)\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.EXTENT,\n            (0, 0, w * 2, h * 2),\n            Image.Resampling.BILINEAR,\n            fillcolor=\"red\",\n        )\n        assert transformed.getpixel((w - 1, h - 1)) == expected_pixel\n\n    def test_mesh(self):\n        # this should be a checkerboard of halfsized hoppers in ul, lr\n        im = hopper(\"RGBA\")\n        (w, h) = im.size\n        transformed = im.transform(\n            im.size,\n            Image.Transform.MESH,\n            (\n                (\n                    (0, 0, w // 2, h // 2),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n                (\n                    (w // 2, h // 2, w, h),  # box\n                    (0, 0, 0, h, w, h, w, 0),  # ul -> ccw around quad\n                ),\n            ),\n            Image.Resampling.BILINEAR,\n        )\n\n        scaled = im.transform(\n            (w // 2, h // 2),\n            Image.Transform.AFFINE,\n            (2, 0, 0, 0, 2, 0),\n            Image.Resampling.BILINEAR,\n        )\n\n        checker = Image.new(\"RGBA\", im.size)\n        checker.paste(scaled, (0, 0))\n        checker.paste(scaled, (w // 2, h // 2))\n\n        assert_image_equal(transformed, checker)\n\n        # now, check to see that the extra area is (0, 0, 0, 0)\n        blank = Image.new(\"RGBA\", (w // 2, h // 2), (0, 0, 0, 0))\n\n        assert_image_equal(blank, transformed.crop((w // 2, 0, w, h // 2)))\n        assert_image_equal(blank, transformed.crop((0, h // 2, w // 2, h)))\n\n    def _test_alpha_premult(self, op):\n        # create image with half white, half black,\n        # with the black half transparent.\n        # do op,\n        # there should be no darkness in the white section.\n        im = Image.new(\"RGBA\", (10, 10), (0, 0, 0, 0))\n        im2 = Image.new(\"RGBA\", (5, 10), (255, 255, 255, 255))\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n        im_background = Image.new(\"RGB\", (40, 10), (255, 255, 255))\n        im_background.paste(im, (0, 0), im)\n\n        hist = im_background.histogram()\n        assert 40 * 10 == hist[-1]\n\n    def test_alpha_premult_resize(self):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.BILINEAR)\n\n        self._test_alpha_premult(op)\n\n    def test_alpha_premult_transform(self):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.BILINEAR\n            )\n\n        self._test_alpha_premult(op)\n\n    def _test_nearest(self, op, mode):\n        # create white image with half transparent,\n        # do op,\n        # the image should remain white with half transparent\n        transparent, opaque = {\n            \"RGBA\": ((255, 255, 255, 0), (255, 255, 255, 255)),\n            \"LA\": ((255, 0), (255, 255)),\n        }[mode]\n        im = Image.new(mode, (10, 10), transparent)\n        im2 = Image.new(mode, (5, 10), opaque)\n        im.paste(im2, (0, 0))\n\n        im = op(im, (40, 10))\n\n        colors = sorted(im.getcolors())\n        assert colors == sorted(\n            (\n                (20 * 10, opaque),\n                (20 * 10, transparent),\n            )\n        )\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_resize(self, mode):\n        def op(im, sz):\n            return im.resize(sz, Image.Resampling.NEAREST)\n\n        self._test_nearest(op, mode)\n\n    @pytest.mark.parametrize(\"mode\", (\"RGBA\", \"LA\"))\n    def test_nearest_transform(self, mode):\n        def op(im, sz):\n            (w, h) = im.size\n            return im.transform(\n                sz, Image.Transform.EXTENT, (0, 0, w, h), Image.Resampling.NEAREST\n            )\n\n        self._test_nearest(op, mode)\n\n    def test_blank_fill(self):\n        # attempting to hit\n        # https://github.com/python-pillow/Pillow/issues/254 reported\n        #\n        # issue is that transforms with transparent overflow area\n        # contained junk from previous images, especially on systems with\n        # constrained memory. So, attempt to fill up memory with a\n        # pattern, free it, and then run the mesh test again. Using a 1Mp\n        # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64\n        # bit 12.04 VM with 512 megs available, this fails with Pillow <\n        # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea\n        #\n        # Running by default, but I'd totally understand not doing it in\n        # the future\n\n        pattern = [Image.new(\"RGBA\", (1024, 1024), (a, a, a, a)) for a in range(1, 65)]\n\n        # Yeah. Watch some JIT optimize this out.\n        pattern = None  # noqa: F841\n\n        self.test_mesh()\n\n    def test_missing_method_data(self):\n        with hopper() as im:\n            with pytest.raises(ValueError):\n                im.transform((100, 100), None)\n\n    @pytest.mark.parametrize(\"resample\", (Image.Resampling.BOX, \"unknown\"))\n    def test_unknown_resampling_filter(self, resample):\n        with hopper() as im:\n            (w, h) = im.size\n            with pytest.raises(ValueError):\n                im.transform((100, 100), Image.Transform.EXTENT, (0, 0, w, h), resample)\n\n\nclass TestImageTransformAffine:\n    transform = Image.Transform.AFFINE\n\n    def _test_image(self):\n        im = hopper(\"RGB\")\n        return im.crop((10, 20, im.width - 10, im.height - 20))\n\n    @pytest.mark.parametrize(\n        \"deg, transpose\",\n        (\n            (0, None),\n            (90, Image.Transpose.ROTATE_90),\n            (180, Image.Transpose.ROTATE_180),\n            (270, Image.Transpose.ROTATE_270),\n        ),\n    )\n    def test_rotate(self, deg, transpose):\n        im = self._test_image()\n\n        angle = -math.radians(deg)\n        matrix = [\n            round(math.cos(angle), 15),\n            round(math.sin(angle), 15),\n            0.0,\n            round(-math.sin(angle), 15),\n            round(math.cos(angle), 15),\n            0.0,\n            0,\n            0,\n        ]\n        matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2\n        matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2\n\n        if transpose is not None:\n            transposed = im.transpose(transpose)\n        else:\n            transposed = im\n\n        for resample in [\n            Image.Resampling.NEAREST,\n            Image.Resampling.BILINEAR,\n            Image.Resampling.BICUBIC,\n        ]:\n            transformed = im.transform(\n                transposed.size, self.transform, matrix, resample\n            )\n            assert_image_equal(transposed, transformed)\n\n    @pytest.mark.parametrize(\n        \"scale, epsilon_scale\",\n        (\n            (1.1, 6.9),\n            (1.5, 5.5),\n            (2.0, 5.5),\n            (2.3, 3.7),\n            (2.5, 3.7),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample,epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 2),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_resize(self, scale, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width * scale)), int(round(im.height * scale))\n        matrix_up = [1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0]\n        matrix_down = [scale, 0, 0, 0, scale, 0, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n    @pytest.mark.parametrize(\n        \"x, y, epsilon_scale\",\n        (\n            (0.1, 0, 3.7),\n            (0.6, 0, 9.1),\n            (50, 50, 0),\n        ),\n    )\n    @pytest.mark.parametrize(\n        \"resample, epsilon\",\n        (\n            (Image.Resampling.NEAREST, 0),\n            (Image.Resampling.BILINEAR, 1.5),\n            (Image.Resampling.BICUBIC, 1),\n        ),\n    )\n    def test_translate(self, x, y, epsilon_scale, resample, epsilon):\n        im = self._test_image()\n\n        size_up = int(round(im.width + x)), int(round(im.height + y))\n        matrix_up = [1, 0, -x, 0, 1, -y, 0, 0]\n        matrix_down = [1, 0, x, 0, 1, y, 0, 0]\n\n        transformed = im.transform(size_up, self.transform, matrix_up, resample)\n        transformed = transformed.transform(\n            im.size, self.transform, matrix_down, resample\n        )\n        assert_image_similar(transformed, im, epsilon * epsilon_scale)\n\n\nclass TestImageTransformPerspective(TestImageTransformAffine):\n    # Repeat all tests for AFFINE transformations with PERSPECTIVE\n    transform = Image.Transform.PERSPECTIVE\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Pillow installation attempts...\nVerifying Pillow installation...\n✅ Pillow installation successful!\nSuccessfully installed Pillow\n",
    "setup_res_stderr": "warning: `VIRTUAL_ENV=/home/gcpuser/r2e-edits-internal/.venv` does not match the project environment path `.venv` and will be ignored\nUsing CPython 3.9.21\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 12 packages in 16ms\nInstalled 12 packages in 190ms\n + coverage==7.6.10\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.16.1\n + pytest==8.3.4\n + pytest-cov==6.0.0\n + setuptools==75.8.0\n + tomli==2.2.1\nResolved 1 package in 994ms\nPrepared 1 package in 35.31s\nInstalled 1 package in 1ms\n + pillow==10.3.0.dev0 (from file:///home/gcpuser/buckets/local_repoeval_bucket/repos/pillow_ba6399cad14d816cafc44c25782d6a3153082ae4)\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "\u001b[1m============================= test session starts ==============================\u001b[0m\nplatform linux -- Python 3.9.21, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/pillow_ba6399cad14d816cafc44c25782d6a3153082ae4\nconfigfile: pyproject.toml\nplugins: cov-6.0.0\ncollected 75 items\n\nr2e_tests/test_1.py 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    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "\u001b[1m============================= test session starts ==============================\u001b[0m\nplatform linux -- Python 3.9.21, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/pillow_ba6399cad14d816cafc44c25782d6a3153082ae4\nconfigfile: pyproject.toml\nplugins: cov-6.0.0\ncollected 75 items\n\nr2e_tests/test_1.py 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      im.transform((100, 100), transform)\n>       transform = ImageTransform.PerspectiveTransform(seq[:8])\n\u001b[1m\u001b[31mE       AttributeError: module 'PIL.ImageTransform' has no attribute 'PerspectiveTransform'\u001b[0m\n\n\u001b[1m\u001b[31mr2e_tests/test_1.py\u001b[0m:19: AttributeError\n==================================== PASSES ====================================\n\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransform::test_info\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransform::test_palette\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransform::test_extent\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransform::test_quad\u001b[0m\n\u001b[32mPASSED\u001b[0m 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r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_rotate[180-Transpose.ROTATE_180]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_rotate[270-Transpose.ROTATE_270]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.NEAREST-0-1.1-6.9]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.NEAREST-0-1.5-5.5]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.NEAREST-0-2.0-5.5]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.NEAREST-0-2.3-3.7]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.NEAREST-0-2.5-3.7]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BILINEAR-2-1.1-6.9]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BILINEAR-2-1.5-5.5]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BILINEAR-2-2.0-5.5]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BILINEAR-2-2.3-3.7]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BILINEAR-2-2.5-3.7]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BICUBIC-1-1.1-6.9]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformAffine::test_resize[Resampling.BICUBIC-1-1.5-5.5]\u001b[0m\n\u001b[32mPASSED\u001b[0m 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r2e_tests/test_1.py::\u001b[1mTestImageTransformPerspective::test_translate[Resampling.BILINEAR-1.5-0.6-0-9.1]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformPerspective::test_translate[Resampling.BILINEAR-1.5-50-50-0]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformPerspective::test_translate[Resampling.BICUBIC-1-0.1-0-3.7]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformPerspective::test_translate[Resampling.BICUBIC-1-0.6-0-9.1]\u001b[0m\n\u001b[32mPASSED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransformPerspective::test_translate[Resampling.BICUBIC-1-50-50-0]\u001b[0m\n\u001b[31mFAILED\u001b[0m r2e_tests/test_1.py::\u001b[1mTestImageTransform::test_sanity\u001b[0m - AttributeError: module 'PIL.ImageTransform' has no attribute 'PerspectiveTr...\n\u001b[31m========================= \u001b[31m\u001b[1m1 failed\u001b[0m, \u001b[32m74 passed\u001b[0m\u001b[31m in 0.43s\u001b[0m\u001b[31m =========================\u001b[0m\n",
    "old_commit_res_stderr": ""
}
Tests/test_image_transform.py docs/reference/ImageTransform.rst src/PIL/ImageTransform.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** PerspectiveTransform Missing in PIL.ImageTransform Causes AttributeError

**Description:**
Attempting to use `PerspectiveTransform` from `PIL.ImageTransform` results in an `AttributeError` because the class is not defined in the module. This prevents users from performing perspective transformations on images as intended.

**Example Code:**
```python
from PIL import Image, ImageTransform

def apply_perspective(image, matrix):
    transform = ImageTransform.PerspectiveTransform(matrix)
    return image.transform((100, 100), transform)

im = Image.new("L", (100, 100))
matrix = (1, 0, 0, 0, 1, 0, 0, 1)
transformed_image = apply_perspective(im, matrix)
```

**Expected Behavior:**
The image should be successfully transformed using the provided perspective matrix without any errors.

**Actual Behavior:**
An `AttributeError` is raised indicating that `PerspectiveTransform` does not exist in the `PIL.ImageTransform` module.

```
AttributeError: module 'PIL.ImageTransform' has no attribute 'PerspectiveTransform'
```
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### src/PIL/ImageTransform.py
#
# The Python Imaging Library.
# $Id$
#
# transform wrappers
#
# History:
# 2002-04-08 fl   Created
#
# Copyright (c) 2002 by Secret Labs AB
# Copyright (c) 2002 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#
from __future__ import annotations

from typing import Sequence

from . import Image


class Transform(Image.ImageTransformHandler):
    """Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`."""

    method: Image.Transform

    def __init__(self, data: Sequence[int]) -> None:
        self.data = data

    def getdata(self) -> tuple[Image.Transform, Sequence[int]]:
        return self.method, self.data

    def transform(
        self,
        size: tuple[int, int],
        image: Image.Image,
        **options: dict[str, str | int | tuple[int, ...] | list[int]],
    ) -> Image.Image:
        """Perform the transform. Called from :py:meth:`.Image.transform`."""
        # can be overridden
        method, data = self.getdata()
        return image.transform(size, method, data, **options)


class AffineTransform(Transform):
    """
    Define an affine image transform.

    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first
    two rows from an affine transform matrix. For each pixel (x, y) in the
    output image, the new value is taken from a position (a x + b y + c,
    d x + e y + f) in the input image, rounded to nearest pixel.

    This function can be used to scale, translate, rotate, and shear the
    original image.

    See :py:meth:`.Image.transform`

    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows
        from an affine transform matrix.
    """

    method = Image.Transform.AFFINE


class ExtentTransform(Transform):
    """
    Define a transform to extract a subregion from an image.

    Maps a rectangle (defined by two corners) from the image to a rectangle of
    the given size. The resulting image will contain data sampled from between
    the corners, such that (x0, y0) in the input image will end up at (0,0) in
    the output image, and (x1, y1) at size.

    This method can be used to crop, stretch, shrink, or mirror an arbitrary
    rectangle in the current image. It is slightly slower than crop, but about
    as fast as a corresponding resize operation.

    See :py:meth:`.Image.transform`

    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the
        input image's coordinate system. See :ref:`coordinate-system`.
    """

    method = Image.Transform.EXTENT


class QuadTransform(Transform):
    """
    Define a quad image transform.

    Maps a quadrilateral (a region defined by four corners) from the image to a
    rectangle of the given size.

    See :py:meth:`.Image.transform`

    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the
        upper left, lower left, lower right, and upper right corner of the
        source quadrilateral.
    """

    method = Image.Transform.QUAD


class MeshTransform(Transform):
    """
    Define a mesh image transform.  A mesh transform consists of one or more
    individual quad transforms.

    See :py:meth:`.Image.transform`

    :param data: A list of (bbox, quad) tuples.
    """

    method = Image.Transform.MESH

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
[ISSUE]
**Title:** PerspectiveTransform Missing in PIL.ImageTransform Causes AttributeError

**Description:**
Attempting to use `PerspectiveTransform` from `PIL.ImageTransform` results in an `AttributeError` because the class is not defined in the module. This prevents users from performing perspective transformations on images as intended.

**Example Code:**
```python
from PIL import Image, ImageTransform

def apply_perspective(image, matrix):
    transform = ImageTransform.PerspectiveTransform(matrix)
    return image.transform((100, 100), transform)

im = Image.new("L", (100, 100))
matrix = (1, 0, 0, 0, 1, 0, 0, 1)
transformed_image = apply_perspective(im, matrix)
```

**Expected Behavior:**
The image should be successfully transformed using the provided perspective matrix without any errors.

**Actual Behavior:**
An `AttributeError` is raised indicating that `PerspectiveTransform` does not exist in the `PIL.ImageTransform` module.

```
AttributeError: module 'PIL.ImageTransform' has no attribute 'PerspectiveTransform'
```
[/ISSUE]
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    "\u001b[1mTestImageTransformPerspective.test_rotate[180-Transpose.ROTATE_180]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_rotate[270-Transpose.ROTATE_270]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.NEAREST-0-1.1-6.9]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.NEAREST-0-1.5-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.NEAREST-0-2.0-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.NEAREST-0-2.3-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.NEAREST-0-2.5-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BILINEAR-2-1.1-6.9]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BILINEAR-2-1.5-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BILINEAR-2-2.0-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BILINEAR-2-2.3-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BILINEAR-2-2.5-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BICUBIC-1-1.1-6.9]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BICUBIC-1-1.5-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BICUBIC-1-2.0-5.5]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BICUBIC-1-2.3-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_resize[Resampling.BICUBIC-1-2.5-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.NEAREST-0-0.1-0-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.NEAREST-0-0.6-0-9.1]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.NEAREST-0-50-50-0]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BILINEAR-1.5-0.1-0-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BILINEAR-1.5-0.6-0-9.1]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BILINEAR-1.5-50-50-0]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BICUBIC-1-0.1-0-3.7]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BICUBIC-1-0.6-0-9.1]\u001b[0m": "PASSED",
    "\u001b[1mTestImageTransformPerspective.test_translate[Resampling.BICUBIC-1-50-50-0]\u001b[0m": "PASSED"
}
diff --git a/src/PIL/ImageTransform.py b/src/PIL/ImageTransform.py
index 4f79500e6..6aa82dadd 100644
--- a/src/PIL/ImageTransform.py
+++ b/src/PIL/ImageTransform.py
@@ -63,6 +63,26 @@ class AffineTransform(Transform):
     method = Image.Transform.AFFINE
 
 
+class PerspectiveTransform(Transform):
+    """
+    Define a perspective image transform.
+
+    This function takes an 8-tuple (a, b, c, d, e, f, g, h). For each pixel
+    (x, y) in the output image, the new value is taken from a position
+    ((a x + b y + c) / (g x + h y + 1), (d x + e y + f) / (g x + h y + 1)) in
+    the input image, rounded to nearest pixel.
+
+    This function can be used to scale, translate, rotate, and shear the
+    original image.
+
+    See :py:meth:`.Image.transform`
+
+    :param matrix: An 8-tuple (a, b, c, d, e, f, g, h).
+    """
+
+    method = Image.Transform.PERSPECTIVE
+
+
 class ExtentTransform(Transform):
     """
     Define a transform to extract a subregion from an image.
 

{"src/PIL/ImageTransform.py": "#\n# The Python Imaging Library.\n# $Id$\n#\n# transform wrappers\n#\n# History:\n# 2002-04-08 fl   Created\n#\n# Copyright (c) 2002 by Secret Labs AB\n# Copyright (c) 2002 by Fredrik Lundh\n#\n# See the README file for information on usage and redistribution.\n#\nfrom __future__ import annotations\n\nfrom typing import Sequence\n\nfrom . import Image\n\n\nclass Transform(Image.ImageTransformHandler):\n    \"\"\"Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`.\"\"\"\n\n    method: Image.Transform\n\n    def __init__(self, data: Sequence[int]) -> None:\n        self.data = data\n\n    def getdata(self) -> tuple[Image.Transform, Sequence[int]]:\n        return self.method, self.data\n\n    def transform(\n        self,\n        size: tuple[int, int],\n        image: Image.Image,\n        **options: dict[str, str | int | tuple[int, ...] | list[int]],\n    ) -> Image.Image:\n        \"\"\"Perform the transform. Called from :py:meth:`.Image.transform`.\"\"\"\n        # can be overridden\n        method, data = self.getdata()\n        return image.transform(size, method, data, **options)\n\n\nclass AffineTransform(Transform):\n    \"\"\"\n    Define an affine image transform.\n\n    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first\n    two rows from an affine transform matrix. For each pixel (x, y) in the\n    output image, the new value is taken from a position (a x + b y + c,\n    d x + e y + f) in the input image, rounded to nearest pixel.\n\n    This function can be used to scale, translate, rotate, and shear the\n    original image.\n\n    See :py:meth:`.Image.transform`\n\n    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows\n        from an affine transform matrix.\n    \"\"\"\n\n    method = Image.Transform.AFFINE\n\n\nclass ExtentTransform(Transform):\n    \"\"\"\n    Define a transform to extract a subregion from an image.\n\n    Maps a rectangle (defined by two corners) from the image to a rectangle of\n    the given size. The resulting image will contain data sampled from between\n    the corners, such that (x0, y0) in the input image will end up at (0,0) in\n    the output image, and (x1, y1) at size.\n\n    This method can be used to crop, stretch, shrink, or mirror an arbitrary\n    rectangle in the current image. It is slightly slower than crop, but about\n    as fast as a corresponding resize operation.\n\n    See :py:meth:`.Image.transform`\n\n    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the\n        input image's coordinate system. See :ref:`coordinate-system`.\n    \"\"\"\n\n    method = Image.Transform.EXTENT\n\n\nclass QuadTransform(Transform):\n    \"\"\"\n    Define a quad image transform.\n\n    Maps a quadrilateral (a region defined by four corners) from the image to a\n    rectangle of the given size.\n\n    See :py:meth:`.Image.transform`\n\n    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the\n        upper left, lower left, lower right, and upper right corner of the\n        source quadrilateral.\n    \"\"\"\n\n    method = Image.Transform.QUAD\n\n\nclass MeshTransform(Transform):\n    \"\"\"\n    Define a mesh image transform.  A mesh transform consists of one or more\n    individual quad transforms.\n\n    See :py:meth:`.Image.transform`\n\n    :param data: A list of (bbox, quad) tuples.\n    \"\"\"\n\n    method = Image.Transform.MESH\n"}
A user will ask you to solve a task. You should first draft your thinking process (inner monologue). Then, generate the solution.

Your response format must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct solution.
</think>
<solution>
Final solution presented to the user.
</solution>
system
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** PerspectiveTransform Missing in PIL.ImageTransform Causes AttributeError

**Description:**
Attempting to use `PerspectiveTransform` from `PIL.ImageTransform` results in an `AttributeError` because the class is not defined in the module. This prevents users from performing perspective transformations on images as intended.

**Example Code:**
```python
from PIL import Image, ImageTransform

def apply_perspective(image, matrix):
    transform = ImageTransform.PerspectiveTransform(matrix)
    return image.transform((100, 100), transform)

im = Image.new("L", (100, 100))
matrix = (1, 0, 0, 0, 1, 0, 0, 1)
transformed_image = apply_perspective(im, matrix)
```

**Expected Behavior:**
The image should be successfully transformed using the provided perspective matrix without any errors.

**Actual Behavior:**
An `AttributeError` is raised indicating that `PerspectiveTransform` does not exist in the `PIL.ImageTransform` module.

```
AttributeError: module 'PIL.ImageTransform' has no attribute 'PerspectiveTransform'
```
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### src/PIL/ImageTransform.py
#
# The Python Imaging Library.
# $Id$
#
# transform wrappers
#
# History:
# 2002-04-08 fl   Created
#
# Copyright (c) 2002 by Secret Labs AB
# Copyright (c) 2002 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#
from __future__ import annotations

from typing import Sequence

from . import Image


class Transform(Image.ImageTransformHandler):
    """Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`."""

    method: Image.Transform

    def __init__(self, data: Sequence[int]) -> None:
        self.data = data

    def getdata(self) -> tuple[Image.Transform, Sequence[int]]:
        return self.method, self.data

    def transform(
        self,
        size: tuple[int, int],
        image: Image.Image,
        **options: dict[str, str | int | tuple[int, ...] | list[int]],
    ) -> Image.Image:
        """Perform the transform. Called from :py:meth:`.Image.transform`."""
        # can be overridden
        method, data = self.getdata()
        return image.transform(size, method, data, **options)


class AffineTransform(Transform):
    """
    Define an affine image transform.

    This function takes a 6-tuple (a, b, c, d, e, f) which contain the first
    two rows from an affine transform matrix. For each pixel (x, y) in the
    output image, the new value is taken from a position (a x + b y + c,
    d x + e y + f) in the input image, rounded to nearest pixel.

    This function can be used to scale, translate, rotate, and shear the
    original image.

    See :py:meth:`.Image.transform`

    :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows
        from an affine transform matrix.
    """

    method = Image.Transform.AFFINE


class ExtentTransform(Transform):
    """
    Define a transform to extract a subregion from an image.

    Maps a rectangle (defined by two corners) from the image to a rectangle of
    the given size. The resulting image will contain data sampled from between
    the corners, such that (x0, y0) in the input image will end up at (0,0) in
    the output image, and (x1, y1) at size.

    This method can be used to crop, stretch, shrink, or mirror an arbitrary
    rectangle in the current image. It is slightly slower than crop, but about
    as fast as a corresponding resize operation.

    See :py:meth:`.Image.transform`

    :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the
        input image's coordinate system. See :ref:`coordinate-system`.
    """

    method = Image.Transform.EXTENT


class QuadTransform(Transform):
    """
    Define a quad image transform.

    Maps a quadrilateral (a region defined by four corners) from the image to a
    rectangle of the given size.

    See :py:meth:`.Image.transform`

    :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the
        upper left, lower left, lower right, and upper right corner of the
        source quadrilateral.
    """

    method = Image.Transform.QUAD


class MeshTransform(Transform):
    """
    Define a mesh image transform.  A mesh transform consists of one or more
    individual quad transforms.

    See :py:meth:`.Image.transform`

    :param data: A list of (bbox, quad) tuples.
    """

    method = Image.Transform.MESH

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
user