





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							['''image_processor''', '''tokenizer''']
      a						       =							'''CLIPImageProcessor'''
      a						       =							('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(		self      ,       a__=None      ,       a__=None      ,       **a__       ):
         A_     :				List[Any]			      =	None
         if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""      ,       a__      ,       )
            A_     :				Any			      =	kwargs.pop("""feature_extractor"""       )

         A_     :				Dict			      =	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""       )
         if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""       )

         super().__init__(a__      ,       a__       )
      def __call__(		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       **a__       ):

         if text is None and images is None:
            raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

         if text is not None:
            A_     :				str			      =	self.tokenizer(a__      ,       return_tensors=a__      ,       **a__       )

         if images is not None:
            A_     :				str			      =	self.image_processor(a__      ,       return_tensors=a__      ,       **a__       )

         if text is not None and images is not None:
            A_     :				Tuple			      =	image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__       )      ,       tensor_type=a__       )
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         return self.tokenizer.batch_decode(*a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         return self.tokenizer.decode(*a__      ,       **a__       )
      @property
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	self.tokenizer.model_input_names
         A_     :				Dict			      =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )
      @property
      def 		_lowerCamelCase      (		self       ):
         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,       a__      ,       )
         return self.image_processor_class






      @property
      def 		_lowerCamelCase      (		self       ):
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,       a__      ,       )
         return self.image_processor













import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DetaImageProcessor

class    _UpperCAmelCase							(		unittest.TestCase     ):
      def __init__(		self      ,       a__      ,       a__=7      ,       a__=3      ,       a__=30      ,       a__=400      ,       a__=True      ,       a__=None      ,       a__=True      ,       a__=[0.5, 0.5, 0.5]      ,       a__=[0.5, 0.5, 0.5]      ,       a__=True      ,       a__=1 / 255      ,       a__=True      ,       ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         A_     :				Optional[int]			      =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
         A_     :				str			      =	parent
         A_     :				Dict			      =	batch_size
         A_     :				str			      =	num_channels
         A_     :				List[str]			      =	min_resolution
         A_     :				List[Any]			      =	max_resolution
         A_     :				Optional[Any]			      =	do_resize
         A_     :				int			      =	size
         A_     :				List[Any]			      =	do_normalize
         A_     :				List[Any]			      =	image_mean
         A_     :				Dict			      =	image_std
         A_     :				Tuple			      =	do_rescale
         A_     :				List[Any]			      =	rescale_factor
         A_     :				Tuple			      =	do_pad
      def 		_lowerCamelCase      (		self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=False       ):
         if not batched:
            A_     :				List[str]			      =	image_inputs[0]
            if isinstance(a__      ,       Image.Image       ):
               A_      ,				A_     :				Tuple			      =	image.size
            else:
               A_      ,				A_     :				List[Any]			      =	image.shape[1], image.shape[2]
            if w < h:
               A_     :				List[str]			      =	int(self.size["""shortest_edge"""] * h / w       )
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
            elif w > h:
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
               A_     :				Union[str, Any]			      =	int(self.size["""shortest_edge"""] * w / h       )
            else:
               A_     :				Optional[Any]			      =	self.size["""shortest_edge"""]
               A_     :				int			      =	self.size["""shortest_edge"""]

         else:
            A_     :				List[Any]			      =	[]
            for image in image_inputs:
               A_      ,				A_     :				Optional[Any]			      =	self.get_expected_values([image]       )
               expected_values.append((expected_height, expected_width)       )
            A_     :				List[Any]			      =	max(a__      ,       key=lambda a__       : item[0]       )[0]
            A_     :				Tuple			      =	max(a__      ,       key=lambda a__       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class    _UpperCAmelCase							(		_lowerCamelCase			,       unittest.TestCase     ):
      a						       =							DetaImageProcessor if is_vision_available() else None
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	DetaImageProcessingTester(self       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(a__      ,       """image_mean"""       )       )
         self.assertTrue(hasattr(a__      ,       """image_std"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_normalize"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_resize"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_rescale"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_pad"""       )       )
         self.assertTrue(hasattr(a__      ,       """size"""       )       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 18, """longest_edge""": 1333}       )
         self.assertEqual(image_processor.do_pad      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         pass
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[Any]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_     :				Any			      =	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__      ,       Image.Image       )

         # Test not batched input
         A_     :				List[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Optional[int]			      =	self.image_processor_tester.get_expected_values(a__       )

         self.assertEqual(
             encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

         # Test batched
         A_      ,				A_     :				Optional[Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

         A_     :				Optional[int]			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_images.shape      ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,       )
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				Any			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_     :				int			      =	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=a__      ,       numpify=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__      ,       np.ndarray       )

         # Test not batched input
         A_     :				Union[str, Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Tuple			      =	self.image_processor_tester.get_expected_values(a__       )

         self.assertEqual(
             encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

         # Test batched
         A_     :				Optional[int]			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Union[str, Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

         self.assertEqual(
             encoded_images.shape      ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,       )
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[str]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_     :				Union[str, Any]			      =	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=a__      ,       torchify=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__      ,       torch.Tensor       )

         # Test not batched input
         A_     :				Optional[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				str			      =	self.image_processor_tester.get_expected_values(a__       )

         self.assertEqual(
             encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

         # Test batched
         A_     :				str			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				List[Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

         self.assertEqual(
             encoded_images.shape      ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,       )
      @slow
      def 		_lowerCamelCase      (		self       ):
         # prepare image and target
         A_     :				int			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""       ) as f:
            A_     :				int			      =	json.loads(f.read()       )

         A_     :				Union[str, Any]			      =	{"""image_id""": 39769, """annotations""": target}

         # encode them
         A_     :				str			      =	DetaImageProcessor()
         A_     :				int			      =	image_processing(images=a__      ,       annotations=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				List[str]			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       a__      ,       atol=1E-4       )       )

         # verify area
         A_     :				int			      =	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       a__       )       )
         # verify boxes
         A_     :				str			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[Any]			      =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				Optional[Any]			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Optional[int]			      =	torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify orig_size
         A_     :				Dict			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Tuple			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






      @slow
      def 		_lowerCamelCase      (		self       ):
         # prepare image, target and masks_path
         A_     :				Any			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,       """r"""       ) as f:
            A_     :				Any			      =	json.loads(f.read()       )

         A_     :				Any			      =	{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

         A_     :				Tuple			      =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

         # encode them
         A_     :				str			      =	DetaImageProcessor(format="""coco_panoptic"""       )
         A_     :				Tuple			      =	image_processing(images=a__      ,       annotations=a__      ,       masks_path=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				str			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       a__      ,       atol=1E-4       )       )

         # verify area
         A_     :				Dict			      =	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       a__       )       )
         # verify boxes
         A_     :				List[Any]			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[str]			      =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				int			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Union[str, Any]			      =	torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify masks
         A_     :				Tuple			      =	822873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       a__       )
         # verify orig_size
         A_     :				Union[str, Any]			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Union[str, Any]			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






