



import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    MODEL_MAPPING,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		)
		from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor






class 	_a			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=100    ,  lowercase_=13    ,  lowercase_=30    ,  lowercase_=2    ,  lowercase_=3    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=32    ,  lowercase_=4    ,  lowercase_=4    ,  lowercase_=37    ,  lowercase_="gelu"    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=10    ,  lowercase_=0.0_2    ,  lowercase_=3    ,  lowercase_=None    ,  lowercase_=[0, 1, 2, 3]    ,  )	->					int:
									lowerCAmelCase	:   str					     =					parent
									lowerCAmelCase	:   List[str]					     =					100
									lowerCAmelCase	:   str					     =					batch_size
									lowerCAmelCase	:   List[Any]					     =					image_size
									lowerCAmelCase	:   Dict					     =					patch_size
									lowerCAmelCase	:   Optional[Any]					     =					num_channels
									lowerCAmelCase	:   List[Any]					     =					is_training
									lowerCAmelCase	:   int					     =					use_labels
									lowerCAmelCase	:   str					     =					hidden_size
									lowerCAmelCase	:   Dict					     =					num_hidden_layers
									lowerCAmelCase	:   str					     =					num_attention_heads
									lowerCAmelCase	:   Tuple					     =					intermediate_size
									lowerCAmelCase	:   Optional[int]					     =					hidden_act
									lowerCAmelCase	:   Tuple					     =					hidden_dropout_prob
									lowerCAmelCase	:   Any					     =					attention_probs_dropout_prob
									lowerCAmelCase	:   Optional[int]					     =					type_sequence_label_size
									lowerCAmelCase	:   List[Any]					     =					initializer_range
									lowerCAmelCase	:   Optional[Any]					     =					scope
									lowerCAmelCase	:   Dict					     =					out_indices
									lowerCAmelCase	:   int					     =					num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowerCAmelCase	:   List[Any]					     =					(image_size // patch_size) ** 2
									lowerCAmelCase	:   Optional[int]					     =					num_patches + 1






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   List[str]					     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									lowerCAmelCase	:   Optional[int]					     =					None
									lowerCAmelCase	:   Optional[int]					     =					None
									if self.use_labels:
															lowerCAmelCase	:   str					     =					ids_tensor([self.batch_size]    ,  self.type_sequence_label_size )
															lowerCAmelCase	:   Any					     =					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,  self.num_labels )

									lowerCAmelCase	:   List[Any]					     =					self.get_config()

									return config, pixel_values, labels, pixel_labels






			def 							_snake_case   (    self )	->					Tuple:
									return DataaVecVisionConfig(
									    vocab_size=self.vocab_size    ,  image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  is_decoder=__SCREAMING_SNAKE_CASE    ,  initializer_range=self.initializer_range    ,  out_indices=self.out_indices    ,  )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Dict:
									lowerCAmelCase	:   Dict					     =					DataaVecVisionModel(config=__SCREAMING_SNAKE_CASE )
									model.to(__SCREAMING_SNAKE_CASE )
									model.eval()
									lowerCAmelCase	:   List[Any]					     =					model(__SCREAMING_SNAKE_CASE )
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									lowerCAmelCase	:   Any					     =					(self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, num_patches + 1, self.hidden_size) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					int:
									lowerCAmelCase	:   Tuple					     =					self.type_sequence_label_size
									lowerCAmelCase	:   Optional[Any]					     =					DataaVecVisionForImageClassification(__SCREAMING_SNAKE_CASE )
									model.to(__SCREAMING_SNAKE_CASE )
									model.eval()
									lowerCAmelCase	:   Any					     =					model(__SCREAMING_SNAKE_CASE    ,  labels=__SCREAMING_SNAKE_CASE )
									self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Any:
									lowerCAmelCase	:   Optional[Any]					     =					self.num_labels
									lowerCAmelCase	:   Optional[Any]					     =					DataaVecVisionForSemanticSegmentation(__SCREAMING_SNAKE_CASE )
									model.to(__SCREAMING_SNAKE_CASE )
									model.eval()
									lowerCAmelCase	:   Optional[int]					     =					model(__SCREAMING_SNAKE_CASE )
									self.parent.assertEqual(
									    result.logits.shape    ,  (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
									lowerCAmelCase	:   int					     =					model(__SCREAMING_SNAKE_CASE    ,  labels=__SCREAMING_SNAKE_CASE )
									self.parent.assertEqual(
									    result.logits.shape    ,  (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Optional[Any]					     =					self.prepare_config_and_inputs()
									lowerCAmelCase	:   Optional[int]					     =					config_and_inputs
									lowerCAmelCase	:   List[Any]					     =					{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class 	_a			(      lowerCamelCase_     , lowerCamelCase_     , unittest.TestCase  ):
			_UpperCamelCase:   List[str]	     =		(
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase:   Union[str, Any]	     =		(
			    {
			        "feature-extraction": DataaVecVisionModel,
			        "image-classification": DataaVecVisionForImageClassification,
			        "image-segmentation": DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase:   int	     =		False
			_UpperCamelCase:   Optional[Any]	     =		False
			_UpperCamelCase:   int	     =		False






			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					DataaVecVisionModelTester(self )
									lowerCAmelCase	:   List[Any]					     =					ConfigTester(
									    self    ,  config_class=__SCREAMING_SNAKE_CASE    ,  has_text_modality=__SCREAMING_SNAKE_CASE    ,  hidden_size=37 )






			def 							_snake_case   (    self )	->					Optional[Any]:
									self.config_tester.run_common_tests()






			def 							_snake_case   (    self )	->					Tuple:
									pass






			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`""" )
			def 							_snake_case   (    self )	->					str:
									pass






			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   Any					     =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															lowerCAmelCase	:   Optional[int]					     =					model_class(__SCREAMING_SNAKE_CASE )
															self.assertIsInstance(model.get_input_embeddings()    ,  (nn.Module) )
															lowerCAmelCase	:   List[Any]					     =					model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE    ,  nn.Linear ) )






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Tuple					     =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															lowerCAmelCase	:   int					     =					model_class(__SCREAMING_SNAKE_CASE )
															lowerCAmelCase	:   List[str]					     =					inspect.signature(model.forward )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															lowerCAmelCase	:   List[Any]					     =					[*signature.parameters.keys()]

															lowerCAmelCase	:   Tuple					     =					['''pixel_values''']
															self.assertListEqual(arg_names[:1]    ,  __SCREAMING_SNAKE_CASE )






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Dict					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )






			def 							_snake_case   (    self )	->					List[Any]:
									lowerCAmelCase	:   List[Any]					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*__SCREAMING_SNAKE_CASE )






			def 							_snake_case   (    self )	->					int:





									if not self.model_tester.is_training:
															return

									lowerCAmelCase	:   Tuple					     =					self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase	:   Tuple					     =					True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(__SCREAMING_SNAKE_CASE )]:
																					continue

															lowerCAmelCase	:   List[Any]					     =					model_class(__SCREAMING_SNAKE_CASE )
															model.to(__SCREAMING_SNAKE_CASE )
															model.train()
															lowerCAmelCase	:   str					     =					self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  return_labels=__SCREAMING_SNAKE_CASE )
															lowerCAmelCase	:   Optional[int]					     =					model(**__SCREAMING_SNAKE_CASE ).loss
															loss.backward()






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase	:   Tuple					     =					self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
															return

									lowerCAmelCase	:   List[Any]					     =					False
									lowerCAmelCase	:   Dict					     =					True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(__SCREAMING_SNAKE_CASE )] or not model_class.supports_gradient_checkpointing:
																					continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																					lowerCAmelCase	:   Optional[Any]					     =					inputs_dict['''pixel_values'''].shape
																					lowerCAmelCase	:   int					     =					torch.zeros(
																					    [self.model_tester.batch_size, height, width]    ,  device=__SCREAMING_SNAKE_CASE ).long()
															lowerCAmelCase	:   List[Any]					     =					model_class(__SCREAMING_SNAKE_CASE )
															model.gradient_checkpointing_enable()
															model.to(__SCREAMING_SNAKE_CASE )
															model.train()
															lowerCAmelCase	:   Dict					     =					self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  return_labels=__SCREAMING_SNAKE_CASE )
															lowerCAmelCase	:   str					     =					model(**__SCREAMING_SNAKE_CASE ).loss
															loss.backward()






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   List[Any]					     =					self.model_tester.prepare_config_and_inputs_for_common()

									lowerCAmelCase	:   List[str]					     =					_config_zero_init(__SCREAMING_SNAKE_CASE )
									for model_class in self.all_model_classes:
															lowerCAmelCase	:   Union[str, Any]					     =					model_class(config=__SCREAMING_SNAKE_CASE )
															for name, param in model.named_parameters():
																					# we skip lambda parameters as these require special initial values
																					# determined by config.layer_scale_init_value
																					if "lambda" in name:
																											continue
																					if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()    ,  [0.0, 1.0]    ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,  )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_=2e-4    ,  lowercase_="outputs"    ,  lowercase_=None )	->					Tuple:
									super().check_pt_tf_outputs(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE )






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   str					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )






			@slow
			def 							_snake_case   (    self )	->					Tuple:





									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase	:   int					     =					DataaVecVisionModel.from_pretrained(__SCREAMING_SNAKE_CASE )
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
						return image






@require_torch
@require_vision
class 	_a			(      unittest.TestCase  ):






			@cached_property
			def 							_snake_case   (    self )	->					Optional[int]:
									return (
									    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k""" ) if is_vision_available() else None
									)






			@slow
			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Optional[int]					     =					DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k""" ).to(
									    __SCREAMING_SNAKE_CASE )

									lowerCAmelCase	:   Dict					     =					self.default_image_processor
									lowerCAmelCase	:   Union[str, Any]					     =					prepare_img()
									lowerCAmelCase	:   List[str]					     =					image_processor(images=__SCREAMING_SNAKE_CASE    ,  return_tensors="""pt""" ).to(__SCREAMING_SNAKE_CASE )

									# forward pass
									with torch.no_grad():
															lowerCAmelCase	:   Tuple					     =					model(**__SCREAMING_SNAKE_CASE )
									lowerCAmelCase	:   List[str]					     =					outputs.logits

									# verify the logits
									lowerCAmelCase	:   List[str]					     =					torch.Size((1, 1000) )
									self.assertEqual(logits.shape    ,  __SCREAMING_SNAKE_CASE )

									lowerCAmelCase	:   Union[str, Any]					     =					torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1] ).to(__SCREAMING_SNAKE_CASE )

									self.assertTrue(torch.allclose(logits[0, :3]    ,  __SCREAMING_SNAKE_CASE    ,  atol=1e-4 ) )

									lowerCAmelCase	:   Union[str, Any]					     =					[model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
									self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()    ,  __SCREAMING_SNAKE_CASE )



'''simple docstring'''







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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=18							,				__SCREAMING_SNAKE_CASE=30							,				__SCREAMING_SNAKE_CASE=4_00							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""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,
											}







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			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'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				Image.Image						)

											# Test not batched input
											lowercase_      :	List[Any]						=			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
											lowercase_      :	List[str]						=			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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	Tuple						=			prepare_image_inputs(self.image_processor_tester							,				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
											lowercase_      :	Dict						=			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
											lowercase_      :	Union[str, Any]						=			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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				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
											lowercase_      :	List[str]						=			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
											lowercase_      :	Optional[int]						=			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'''],
											    )							,				)



