


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
						import torch

						from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin
class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:str=None			,			lowerCAmelCase__			:str=None			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str="resnet50"			,			lowerCAmelCase__			:Optional[int]=3			,			lowerCAmelCase__			:Union[str, Any]=32			,			lowerCAmelCase__			:Optional[Any]=3			,			lowerCAmelCase__			:int=True			,			lowerCAmelCase__			:Optional[Any]=True			,			)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		parent
										__SCREAMING_SNAKE_CASE						:     Dict					=		out_indices if out_indices is not None else [4]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		stage_names
										__SCREAMING_SNAKE_CASE						:     Dict					=		out_features
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		backbone
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		image_size
										__SCREAMING_SNAKE_CASE						:     str					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Dict					=		use_pretrained_backbone
										__SCREAMING_SNAKE_CASE						:     Tuple					=		is_training


				def 		__magic_name__(       self			:Optional[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.get_config()

										return config, pixel_values


				def 		__magic_name__(       self			:Dict	)		->			Dict:
										return TimmBackboneConfig(
										    image_size=self.image_size			,			num_channels=self.num_channels			,			out_features=self.out_features			,			out_indices=self.out_indices			,			stage_names=self.stage_names			,			use_pretrained_backbone=self.use_pretrained_backbone			,			backbone=self.backbone			,			)


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Optional[int]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Any					=		TimmBackbone(config=lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()
										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:     List[str]					=		model(lowerCAmelCase__	)
										self.parent.assertEqual(
										    result.feature_map[-1].shape			,			(self.batch_size, model.channels[-1], 14, 14)			,			)



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




@require_torch
@require_timm
class        _lowercase      (			A__						,			A__						,			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[Any]						=		(TimmBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__				:			List[str]						=		{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
				SCREAMING_SNAKE_CASE__				:			List[Any]						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		False


				def 		__magic_name__(       self			:Any	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		TimmBackboneModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		ConfigTester(self			,			config_class=lowerCAmelCase__			,			has_text_modality=lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Any					=		'''resnet18'''
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''microsoft/resnet-18'''

										__SCREAMING_SNAKE_CASE						:     str					=		AutoBackbone.from_pretrained(lowerCAmelCase__			,			use_timm_backbone=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoBackbone.from_pretrained(lowerCAmelCase__	)

										self.assertEqual(len(timm_model.out_features	)			,			len(transformers_model.out_features	)	)
										self.assertEqual(len(timm_model.stage_names	)			,			len(transformers_model.stage_names	)	)
										self.assertEqual(timm_model.channels			,			transformers_model.channels	)
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices			,			(-1,)	)
										self.assertEqual(transformers_model.out_indices			,			[len(timm_model.stage_names	) - 1]	)

										__SCREAMING_SNAKE_CASE						:     Any					=		AutoBackbone.from_pretrained(lowerCAmelCase__			,			use_timm_backbone=lowerCAmelCase__			,			out_indices=[1, 2, 3]	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		AutoBackbone.from_pretrained(lowerCAmelCase__			,			out_indices=[1, 2, 3]	)

										self.assertEqual(timm_model.out_indices			,			transformers_model.out_indices	)
										self.assertEqual(len(timm_model.out_features	)			,			len(transformers_model.out_features	)	)
										self.assertEqual(timm_model.channels			,			transformers_model.channels	)


				@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''	)
				def 		__magic_name__(       self			:str	)		->			List[Any]:
										pass


				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''	)
				def 		__magic_name__(       self			:Tuple	)		->			Any:
										pass


				@unittest.skip('''TimmBackbone initialization is managed on the timm side'''	)
				def 		__magic_name__(       self			:List[str]	)		->			int:
										pass


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''	)
				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										pass


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''	)
				def 		__magic_name__(       self			:str	)		->			str:
										pass


				@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''	)
				def 		__magic_name__(       self			:Dict	)		->			Tuple:
										pass


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''	)
				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										pass


				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''	)
				def 		__magic_name__(       self			:Dict	)		->			Optional[Any]:
										pass


				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''	)
				def 		__magic_name__(       self			:Union[str, Any]	)		->			int:
										pass


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''	)
				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										pass


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''	)
				def 		__magic_name__(       self			:Dict	)		->			str:
										pass


				@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''	)
				def 		__magic_name__(       self			:Dict	)		->			Any:
										pass


				@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''	)
				def 		__magic_name__(       self			:str	)		->			Tuple:
										pass


				@unittest.skip('''Safetensors is not supported by timm.'''	)
				def 		__magic_name__(       self			:int	)		->			str:
										pass


				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Union[str, Any]:
										pass


				def 		__magic_name__(       self			:List[Any]	)		->			List[str]:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		self.model_tester.prepare_config_and_inputs_for_common()

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

																__SCREAMING_SNAKE_CASE						:     str					=		['''pixel_values''']
																self.assertListEqual(arg_names[:1]			,			lowerCAmelCase__	)


				def 		__magic_name__(       self			:Dict	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		self.model_tester.prepare_config_and_inputs_for_common()
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		True
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.has_attentions

										# no need to test all models as different heads yield the same functionality
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.all_model_classes[0]
										__SCREAMING_SNAKE_CASE						:     int					=		model_class(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Any					=		model(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Any					=		outputs[0][-1]

										# Encoder-/Decoder-only models
										__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=lowerCAmelCase__	)

										self.assertIsNotNone(hidden_states.grad	)

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad	)



				def 		__magic_name__(       self			:Optional[Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE						:     Any					=		model_class(lowerCAmelCase__	)
																model.to(lowerCAmelCase__	)
																model.eval()
																__SCREAMING_SNAKE_CASE						:     Any					=		model(**lowerCAmelCase__	)

																self.assertEqual(len(result.feature_maps	)			,			len(config.out_indices	)	)
																self.assertEqual(len(model.channels	)			,			len(config.out_indices	)	)

																# Check output of last stage is taken if out_features=None, out_indices=None
																__SCREAMING_SNAKE_CASE						:     Dict					=		copy.deepcopy(lowerCAmelCase__	)
																__SCREAMING_SNAKE_CASE						:     Dict					=		None
																__SCREAMING_SNAKE_CASE						:     str					=		model_class(lowerCAmelCase__	)
																model.to(lowerCAmelCase__	)
																model.eval()
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(**lowerCAmelCase__	)

																self.assertEqual(len(result.feature_maps	)			,			1	)
																self.assertEqual(len(model.channels	)			,			1	)

																# Check backbone can be initialized with fresh weights
																__SCREAMING_SNAKE_CASE						:     Dict					=		copy.deepcopy(lowerCAmelCase__	)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		False
																__SCREAMING_SNAKE_CASE						:     List[str]					=		model_class(lowerCAmelCase__	)
																model.to(lowerCAmelCase__	)
																model.eval()
																__SCREAMING_SNAKE_CASE						:     List[str]					=		model(**lowerCAmelCase__	)



'''simple docstring'''
import unittest

import numpy as np
import requests

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

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     lowerCamelCase :      int					 =     False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor







class 							A__    (       unittest.TestCase  ):





  def __init__(		self      :	List[str]							,					_a      :	List[Any]							,					_a      :	List[str]=7							,					_a      :	List[str]=3							,					_a      :	Tuple=18							,					_a      :	Tuple=30							,					_a      :	str=400							,					_a      :	Tuple=None							,					_a      :	Union[str, Any]=True							,					_a      :	List[str]=True							,					_a      :	Optional[int]=None							,					)	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size if size is not None else {'height': 20, 'width': 20}
       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =min_resolution
       _SCREAMING_SNAKE_CASE							    =max_resolution
       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =do_normalize
       _SCREAMING_SNAKE_CASE							    =do_convert_rgb
       _SCREAMING_SNAKE_CASE							    =[512, 1024, 2048, 4096]
       _SCREAMING_SNAKE_CASE							    =patch_size if patch_size is not None else {'height': 16, 'width': 16}





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





  def 						A  (		self      :	int )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(_a							,					stream=_a ).raw ).convert('RGB' )
       return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self )





  @property
  def 						A  (		self      :	Optional[Any] )	->    int:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	Any )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processor_tester.prepare_dummy_image()

       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       _SCREAMING_SNAKE_CASE							    =2048

       _SCREAMING_SNAKE_CASE							    =image_processor(_a							,					return_tensors='pt'							,					max_patches=_a )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,					torch.tensor(0.06_06 )							,					atol=1e-3							,					rtol=1e-3 ) )





  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       _SCREAMING_SNAKE_CASE							    =True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(_a ):
                 _SCREAMING_SNAKE_CASE							    =image_processor(
                     image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches

            _SCREAMING_SNAKE_CASE							    ='Hello'

            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	List[Any] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a							,					numpify=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					np.ndarray )

       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	str )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self							,					num_channels=4 )
       _SCREAMING_SNAKE_CASE							    =3





  @property
  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)
