import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
						from PIL import Image
else:






						class      __lowercase					:

												@staticmethod
												def        __a		(	*__lowerCamelCase				:      Optional[Any]							,							**__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


															'''simple docstring'''

															pass






@is_pipeline_test
@require_vision
class      __lowercase					(    unittest.TestCase    ):

						@require_torch
						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									lowercase							=		pipeline(
									    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''							,							)
									lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
									lowercase							=		image_classifier(__lowerCamelCase							,							candidate_labels=['''a''', '''b''', '''c''']   )

									# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
									# python and torch versions.
									self.assertIn(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
									        [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
									    ]							,							)

									lowercase							=		image_classifier([image] * 5							,							candidate_labels=['''A''', '''B''', '''C''']							,							batch_size=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									    ]							,							)

						@require_tf
						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		pipeline(
									    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''							,							framework='''tf'''   )
									lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
									lowercase							=		image_classifier(__lowerCamelCase							,							candidate_labels=['''a''', '''b''', '''c''']   )

									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]							,							)

									lowercase							=		image_classifier([image] * 5							,							candidate_labels=['''A''', '''B''', '''C''']							,							batch_size=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									        [
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									            {'''score''': 0.333, '''label''': ANY(__lowerCamelCase   )},
									        ],
									    ]							,							)

						@slow
						@require_torch
						def        __a		(	self				:      List[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		pipeline(
									    task='''zero-shot-image-classification'''							,							model='''openai/clip-vit-base-patch32'''							,							)
									# This is an image of 2 cats with remotes and no planes
									lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
									lowercase							=		image_classifier(__lowerCamelCase							,							candidate_labels=['''cat''', '''plane''', '''remote''']   )

									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        {'''score''': 0.511, '''label''': '''remote'''},
									        {'''score''': 0.485, '''label''': '''cat'''},
									        {'''score''': 0.004, '''label''': '''plane'''},
									    ]							,							)

									lowercase							=		image_classifier([image] * 5							,							candidate_labels=['''cat''', '''plane''', '''remote''']							,							batch_size=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [
									            {'''score''': 0.511, '''label''': '''remote'''},
									            {'''score''': 0.485, '''label''': '''cat'''},
									            {'''score''': 0.004, '''label''': '''plane'''},
									        ],
									    ]
									    * 5							,							)



						@slow
						@require_tf
						def        __a		(	self				:      List[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		pipeline(
									    task='''zero-shot-image-classification'''							,							model='''openai/clip-vit-base-patch32'''							,							framework='''tf'''   )
									# This is an image of 2 cats with remotes and no planes
									lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
									lowercase							=		image_classifier(__lowerCamelCase							,							candidate_labels=['''cat''', '''plane''', '''remote''']   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        {'''score''': 0.511, '''label''': '''remote'''},
									        {'''score''': 0.485, '''label''': '''cat'''},
									        {'''score''': 0.004, '''label''': '''plane'''},
									    ]							,							)

									lowercase							=		image_classifier([image] * 5							,							candidate_labels=['''cat''', '''plane''', '''remote''']							,							batch_size=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [
									            {'''score''': 0.511, '''label''': '''remote'''},
									            {'''score''': 0.485, '''label''': '''cat'''},
									            {'''score''': 0.004, '''label''': '''plane'''},
									        ],
									    ]
									    * 5							,							)






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





