



"""simple docstring"""






import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		_lowercase					:					int 		=		False


if is_vision_available():
		from PIL import Image
else:

		class 			__SCREAMING_SNAKE_CASE      :


							'''simple docstring'''





							@staticmethod
							def 						snake_case			(			*lowerCamelCase				:    Optional[int],    **lowerCamelCase				:    int	)->   Dict:
										pass

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


					'''simple docstring'''





					_a       =				MODEL_FOR_VISION_2_SEQ_MAPPING
					_a       =				TF_MODEL_FOR_VISION_2_SEQ_MAPPING



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   Union[str, Any]:
								lowerCamelCase__						:						Dict						      =pipeline('''image-to-text''',    model=lowerCamelCase,    tokenizer=lowerCamelCase,    image_processor=lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =[
								    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	),
								    '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
								]
								return pipe, examples



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Optional[Any]	)->   List[str]:
								lowerCamelCase__						:						Any						      =pipe(lowerCamelCase	)
								self.assertEqual(
								    lowerCamelCase,    [
								        [{'''generated_text''': ANY(lowerCamelCase	)}],
								        [{'''generated_text''': ANY(lowerCamelCase	)}],
								    ],    )



					@require_tf
					def 						snake_case			(			self				:    Dict	)->   Optional[Any]:
								lowerCamelCase__						:						List[str]						      =pipeline('''image-to-text''',    model='''hf-internal-testing/tiny-random-vit-gpt2''',    framework='''tf'''	)
								lowerCamelCase__						:						Dict						      ='''./tests/fixtures/tests_samples/COCO/000000039769.png'''

								lowerCamelCase__						:						Optional[Any]						      =pipe(lowerCamelCase	)
								self.assertEqual(
								    lowerCamelCase,    [
								        {
								            '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								        },
								    ],    )

								lowerCamelCase__						:						Tuple						      =pipe([image, image]	)
								self.assertEqual(
								    lowerCamelCase,    [
								        [
								            {
								                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								            }
								        ],
								        [
								            {
								                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								            }
								        ],
								    ],    )

								lowerCamelCase__						:						List[str]						      =pipe(lowerCamelCase,    max_new_tokens=1	)
								self.assertEqual(
								    lowerCamelCase,    [{'''generated_text''': '''growth'''}],    )



					@require_torch
					def 						snake_case			(			self				:    int	)->   Optional[Any]:
								lowerCamelCase__						:						Union[str, Any]						      =pipeline('''image-to-text''',    model='''hf-internal-testing/tiny-random-vit-gpt2'''	)
								lowerCamelCase__						:						str						      ='''./tests/fixtures/tests_samples/COCO/000000039769.png'''

								lowerCamelCase__						:						List[Any]						      =pipe(lowerCamelCase	)
								self.assertEqual(
								    lowerCamelCase,    [
								        {
								            '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								        },
								    ],    )

								lowerCamelCase__						:						Any						      =pipe([image, image]	)
								self.assertEqual(
								    lowerCamelCase,    [
								        [
								            {
								                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								            }
								        ],
								        [
								            {
								                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
								            }
								        ],
								    ],    )



					@require_torch
					def 						snake_case			(			self				:    Any	)->   int:
								lowerCamelCase__						:						Tuple						      =pipeline('''image-to-text''',    model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''	)
								lowerCamelCase__						:						Dict						      ='''./tests/fixtures/tests_samples/COCO/000000039769.png'''
								lowerCamelCase__						:						List[str]						      ='''a photo of'''

								lowerCamelCase__						:						Optional[Any]						      =pipe(lowerCamelCase,    prompt=lowerCamelCase	)
								self.assertTrue(outputs[0]['''generated_text'''].startswith(lowerCamelCase	)	)



					@slow
					@require_torch
					def 						snake_case			(			self				:    Optional[int]	)->   Union[str, Any]:
								lowerCamelCase__						:						Union[str, Any]						      =pipeline('''image-to-text''',    model='''ydshieh/vit-gpt2-coco-en'''	)
								lowerCamelCase__						:						str						      ='''./tests/fixtures/tests_samples/COCO/000000039769.png'''

								lowerCamelCase__						:						Optional[int]						      =pipe(lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]	)

								lowerCamelCase__						:						Union[str, Any]						      =pipe([image, image]	)
								self.assertEqual(
								    lowerCamelCase,    [
								        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
								        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
								    ],    )



					@slow
					@require_torch
					def 						snake_case			(			self				:    Optional[int]	)->   Tuple:
								lowerCamelCase__						:						Any						      =pipeline('''image-to-text''',    model='''Salesforce/blip-image-captioning-base'''	)
								lowerCamelCase__						:						Union[str, Any]						      ='''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
								lowerCamelCase__						:						Optional[int]						      =Image.open(requests.get(lowerCamelCase,    stream=lowerCamelCase	).raw	)

								lowerCamelCase__						:						List[Any]						      =pipe(lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]	)



					@slow
					@require_torch
					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								lowerCamelCase__						:						int						      =pipeline('''image-to-text''',    model='''microsoft/git-base-coco'''	)
								lowerCamelCase__						:						Any						      ='''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
								lowerCamelCase__						:						Any						      =Image.open(requests.get(lowerCamelCase,    stream=lowerCamelCase	).raw	)

								lowerCamelCase__						:						List[str]						      =pipe(lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a cartoon of a purple character.'''}]	)



					@slow
					@require_torch
					def 						snake_case			(			self				:    Union[str, Any]	)->   Optional[int]:
								lowerCamelCase__						:						int						      =pipeline('''image-to-text''',    model='''Salesforce/blip-image-captioning-base'''	)
								lowerCamelCase__						:						Union[str, Any]						      ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
								lowerCamelCase__						:						Dict						      =Image.open(requests.get(lowerCamelCase,    stream=lowerCamelCase	).raw	)

								lowerCamelCase__						:						List[Any]						      ='''a photography of'''

								lowerCamelCase__						:						Union[str, Any]						      =pipe(lowerCamelCase,    prompt=lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a photography of a volcano'''}]	)

								with self.assertRaises(lowerCamelCase	):
											lowerCamelCase__						:						str						      =pipe([image, image],    prompt=[prompt, prompt]	)



					@slow
					@require_torch
					def 						snake_case			(			self				:    str	)->   Dict:
								lowerCamelCase__						:						List[Any]						      =pipeline('''image-to-text''',    model='''microsoft/git-base-coco'''	)
								lowerCamelCase__						:						Optional[int]						      ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
								lowerCamelCase__						:						Any						      =Image.open(requests.get(lowerCamelCase,    stream=lowerCamelCase	).raw	)

								lowerCamelCase__						:						Union[str, Any]						      ='''a photo of a'''

								lowerCamelCase__						:						str						      =pipe(lowerCamelCase,    prompt=lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a photo of a tent with a tent and a tent in the background.'''}]	)

								with self.assertRaises(lowerCamelCase	):
											lowerCamelCase__						:						Dict						      =pipe([image, image],    prompt=[prompt, prompt]	)



					@unittest.skipIf(
					    not is_torch_greater_or_equal_than_1_11,    reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	)
					@slow
					@require_torch
					def 						snake_case			(			self				:    str	)->   Tuple:
								lowerCamelCase__						:						Tuple						      =pipeline('''image-to-text''',    model='''google/pix2struct-ai2d-base'''	)
								lowerCamelCase__						:						Optional[int]						      ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
								lowerCamelCase__						:						Any						      =Image.open(requests.get(lowerCamelCase,    stream=lowerCamelCase	).raw	)

								lowerCamelCase__						:						Tuple						      ='''What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'''

								lowerCamelCase__						:						str						      =pipe(lowerCamelCase,    prompt=lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''ash cloud'''}]	)

								with self.assertRaises(lowerCamelCase	):
											lowerCamelCase__						:						str						      =pipe([image, image],    prompt=[prompt, prompt]	)




					@slow
					@require_tf
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								lowerCamelCase__						:						List[Any]						      =pipeline('''image-to-text''',    model='''ydshieh/vit-gpt2-coco-en''',    framework='''tf'''	)
								lowerCamelCase__						:						Optional[Any]						      ='''./tests/fixtures/tests_samples/COCO/000000039769.png'''

								lowerCamelCase__						:						Dict						      =pipe(lowerCamelCase	)
								self.assertEqual(lowerCamelCase,    [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]	)

								lowerCamelCase__						:						Union[str, Any]						      =pipe([image, image]	)
								self.assertEqual(
								    lowerCamelCase,    [
								        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
								        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
								    ],    )












import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=7    ,_SCREAMING_SNAKE_CASE		:							List[Any]=3    ,_SCREAMING_SNAKE_CASE		:							int=1_8    ,_SCREAMING_SNAKE_CASE		:							Tuple=3_0    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.48145466, 0.4578275, 0.40821073]    ,_SCREAMING_SNAKE_CASE		:							int=[0.26862954, 0.26130258, 0.27577711]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=True    ,)     -> List[str]:


						'''simple docstring'''
						A									=   size if size is not None else {'shortest_edge': 2_0}
						A									=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std
						A									=   do_convert_rgb
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}







		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str=False    ,_SCREAMING_SNAKE_CASE		:							List[str]=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=False		)     -> str:


						'''simple docstring'''
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
										A									=   []
										for i in range(self.batch_size		):
														image_inputs.append(
														    np.random.randint(
														        2_5_5    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta		)		)
						else:
										A									=   []
										for i in range(self.batch_size		):
														A			,		A									=   np.random.choice(np.arange(self.min_resolution    ,self.max_resolution		)    ,2		)
														image_inputs.append(np.random.randint(2_5_5    ,size=(self.num_channels, width, height)    ,dtype=np.uinta		)		)

						if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										A									=   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,0    ,-1		)		) for x in image_inputs]

						if torchify:
										A									=   [torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

						return image_inputs


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Optional[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self		)
		@property
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Optional[int]		)     -> Tuple:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 2_0}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2    ,crop_size=8_4		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 4_2}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 8_4, 'width': 8_4}		)
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						pass
		def    A(	self		:							Tuple		)     -> Optional[int]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)
		def    A(	self		:							int		)     -> Tuple:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)







		def    A(	self		:							str		)     -> str:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Tuple		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self    ,num_channels=4		)
						A									=   3
		@property
		def    A(	self		:							str		)     -> Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Any		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						pass







		def    A(	self		:							Union[str, Any]		)     -> Dict:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)





