
"""simple docstring"""




import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase__      :    List[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    int    							= {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 	lowerCAmelCase_						(UpperCAmelCase__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   """bart"""
				__UpperCamelCase      :						Union[str, Any]					       =   ["""past_key_values"""]
				__UpperCamelCase      :						Union[str, Any]					       =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=5_02_65   ,       SCREAMING_SNAKE_CASE__=10_24   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=40_96   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=40_96   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=10_24   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=2   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   max_position_embeddings
											SCREAMING_SNAKE_CASE__						:       Tuple           =   d_model
											SCREAMING_SNAKE_CASE__						:       Dict           =   encoder_ffn_dim
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   encoder_layers
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   encoder_attention_heads
											SCREAMING_SNAKE_CASE__						:       Any           =   decoder_ffn_dim
											SCREAMING_SNAKE_CASE__						:       str           =   decoder_layers
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   decoder_attention_heads
											SCREAMING_SNAKE_CASE__						:       List[str]           =   dropout
											SCREAMING_SNAKE_CASE__						:       Any           =   attention_dropout
											SCREAMING_SNAKE_CASE__						:       int           =   activation_dropout
											SCREAMING_SNAKE_CASE__						:       str           =   activation_function
											SCREAMING_SNAKE_CASE__						:       Dict           =   init_std
											SCREAMING_SNAKE_CASE__						:       Dict           =   encoder_layerdrop
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   decoder_layerdrop
											SCREAMING_SNAKE_CASE__						:       List[str]           =   classifier_dropout
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   use_cache
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   encoder_layers
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    num_labels=__lowercase   ,       pad_token_id=__lowercase   ,       bos_token_id=__lowercase   ,       eos_token_id=__lowercase   ,       is_encoder_decoder=__lowercase   ,       decoder_start_token_id=__lowercase   ,       forced_eos_token_id=__lowercase   ,       **__lowercase   ,       )

											# ensure backward compatibility for BART CNN models
											if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""   ,       __lowercase    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.bos_token_id
																		warnings.warn(
																		    F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
																		    """The config can simply be saved and uploaded again to be fixed."""    )






class 	lowerCAmelCase_						(UpperCAmelCase__  ):


				"""simple docstring"""



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		    ]    )

																		if self.use_past:
																									SCREAMING_SNAKE_CASE__						:       int           =   {0: '''batch'''}
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																		else:
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   {0: '''batch''', 1: '''decoder_sequence'''}
																									SCREAMING_SNAKE_CASE__						:       List[str]           =   {0: '''batch''', 1: '''decoder_sequence'''}

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase   ,       direction="""inputs"""    )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		    ]    )
																		if self.use_past:
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.num_layers
																									for i in range(__lowercase    ):
																																SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																																SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
																		        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
																		    ]    )

											return common_inputs



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   super().outputs
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   super(__lowercase   ,       self    ).outputs
																		if self.use_past:
																									SCREAMING_SNAKE_CASE__						:       int           =   self.num_layers
																									for i in range(__lowercase    ):
																																SCREAMING_SNAKE_CASE__						:       str           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																																SCREAMING_SNAKE_CASE__						:       str           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Mapping[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase    )

											# Generate decoder inputs
											SCREAMING_SNAKE_CASE__						:       Any           =   seq_length if not self.use_past else 1
											SCREAMING_SNAKE_CASE__						:       Dict           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											SCREAMING_SNAKE_CASE__						:       Dict           =   dict(**__lowercase   ,       **__lowercase    )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
																		else:
																									import torch
																		SCREAMING_SNAKE_CASE__						:       str           =   common_inputs['''input_ids'''].shape
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   common_inputs['''decoder_input_ids'''].shape[1]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.num_attention_heads
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		SCREAMING_SNAKE_CASE__						:       str           =   decoder_seq_length + 3
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		SCREAMING_SNAKE_CASE__						:       Dict           =   torch.cat(
																		    [common_inputs["""decoder_attention_mask"""], torch.ones(__lowercase   ,       __lowercase    )]   ,       dim=1    )

																		SCREAMING_SNAKE_CASE__						:       Dict           =   []
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.num_layers
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   min(__lowercase   ,       __lowercase    )
																		SCREAMING_SNAKE_CASE__						:       Any           =   max(__lowercase   ,       __lowercase    ) - min_num_layers
																		SCREAMING_SNAKE_CASE__						:       int           =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(__lowercase    ):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(__lowercase    ),
																									        torch.zeros(__lowercase    ),
																									        torch.zeros(__lowercase    ),
																									        torch.zeros(__lowercase    ),
																									    )    )
																		# TODO: test this.
																		SCREAMING_SNAKE_CASE__						:       int           =   encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(__lowercase   ,       __lowercase    ):
																									common_inputs["past_key_values"].append((torch.zeros(__lowercase    ), torch.zeros(__lowercase    ))    )
											return common_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Mapping[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase    )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
																		else:
																									import torch
																		SCREAMING_SNAKE_CASE__						:       Dict           =   common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		SCREAMING_SNAKE_CASE__						:       int           =   seqlen + 2
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.num_layers
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.num_attention_heads
																		SCREAMING_SNAKE_CASE__						:       Any           =   (
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		SCREAMING_SNAKE_CASE__						:       str           =   common_inputs['''attention_mask'''].dtype
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.cat(
																		    [common_inputs["""attention_mask"""], torch.ones(__lowercase   ,       __lowercase   ,       dtype=__lowercase    )]   ,       dim=1    )
																		SCREAMING_SNAKE_CASE__						:       int           =   [
																		    (torch.zeros(__lowercase    ), torch.zeros(__lowercase    )) for _ in range(__lowercase    )
																		]
											return common_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Mapping[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   compute_effective_axis_dimension(
											    __lowercase   ,       fixed_dimension=OnnxConfig.default_fixed_batch   ,       num_token_to_add=0    )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer.num_special_tokens_to_add(__lowercase    )
											SCREAMING_SNAKE_CASE__						:       int           =   compute_effective_axis_dimension(
											    __lowercase   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=__lowercase    )

											# Generate dummy inputs according to compute batch and sequence
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
											SCREAMING_SNAKE_CASE__						:       Any           =   dict(tokenizer(__lowercase   ,       return_tensors=__lowercase    )    )
											return common_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Mapping[str, Any]:



											"""simple docstring"""




											if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase   ,       batch_size=__lowercase   ,       seq_length=__lowercase   ,       is_pair=__lowercase   ,       framework=__lowercase    )

											elif self.task == "causal-lm":
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase   ,       batch_size=__lowercase   ,       seq_length=__lowercase   ,       is_pair=__lowercase   ,       framework=__lowercase    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																		    __lowercase   ,       batch_size=__lowercase   ,       seq_length=__lowercase   ,       is_pair=__lowercase   ,       framework=__lowercase    )

											return common_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   super()._flatten_past_key_values_(__lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   super(__lowercase   ,       self    )._flatten_past_key_values_(
																		    __lowercase   ,       __lowercase   ,       __lowercase   ,       __lowercase    )








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 MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







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







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




						@property
						def   lowercase_       ( self							)    ->			Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''size'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = 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}							)

										lowerCAmelCase_       :					Tuple			 = 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   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  Image.Image							)

										# Test not batched input
										lowerCAmelCase_       :					Tuple			 = 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
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  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   lowercase_       ( self							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  np.ndarray							)

										# Test not batched input
										lowerCAmelCase_       :					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
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  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   lowercase_       ( self							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_       :					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
										lowerCAmelCase_       :					Dict			 = image_processing(__lowercase      ,  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'''],
										    )      ,  )