




'''simple docstring'''






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


A_ 			=		logging.get_logger(__name__)

A_ 			=		{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "mbart"
     A_       							= ["past_key_values"]
     A_       							= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(    self    :				Any    ,       __lowerCamelCase    :				Dict=50265    ,       __lowerCamelCase    :				Optional[int]=1024    ,       __lowerCamelCase    :				Any=12    ,       __lowerCamelCase    :				Union[str, Any]=4096    ,       __lowerCamelCase    :				str=16    ,       __lowerCamelCase    :				Any=12    ,       __lowerCamelCase    :				str=4096    ,       __lowerCamelCase    :				str=16    ,       __lowerCamelCase    :				List[Any]=0.0    ,       __lowerCamelCase    :				List[Any]=0.0    ,       __lowerCamelCase    :				int=True    ,       __lowerCamelCase    :				Optional[int]=True    ,       __lowerCamelCase    :				Dict="gelu"    ,       __lowerCamelCase    :				List[Any]=1024    ,       __lowerCamelCase    :				Optional[Any]=0.1    ,       __lowerCamelCase    :				List[str]=0.0    ,       __lowerCamelCase    :				Any=0.0    ,       __lowerCamelCase    :				List[str]=0.0_2    ,       __lowerCamelCase    :				Dict=0.0    ,       __lowerCamelCase    :				int=False    ,       __lowerCamelCase    :				int=1    ,       __lowerCamelCase    :				str=0    ,       __lowerCamelCase    :				Dict=2    ,       __lowerCamelCase    :				List[Any]=2    ,       **__lowerCamelCase    :				List[Any]    ,       ):
           snake_case__					:      Any  			=						vocab_size
           snake_case__					:      Optional[int]  			=						max_position_embeddings
           snake_case__					:      int  			=						d_model
           snake_case__					:      List[Any]  			=						encoder_ffn_dim
           snake_case__					:      List[str]  			=						encoder_layers
           snake_case__					:      str  			=						encoder_attention_heads
           snake_case__					:      List[str]  			=						decoder_ffn_dim
           snake_case__					:      Optional[int]  			=						decoder_layers
           snake_case__					:      List[str]  			=						decoder_attention_heads
           snake_case__					:      Optional[int]  			=						dropout
           snake_case__					:      Optional[Any]  			=						attention_dropout
           snake_case__					:      Optional[int]  			=						activation_dropout
           snake_case__					:      Dict  			=						activation_function
           snake_case__					:      str  			=						init_std
           snake_case__					:      Optional[Any]  			=						encoder_layerdrop
           snake_case__					:      Any  			=						decoder_layerdrop
           snake_case__					:      Union[str, Any]  			=						classifier_dropout
           snake_case__					:      Tuple  			=						use_cache
           snake_case__					:      List[Any]  			=						encoder_layers
           snake_case__					:      int  			=						scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=__lowerCamelCase    ,       bos_token_id=__lowerCamelCase    ,       eos_token_id=__lowerCamelCase    ,       is_encoder_decoder=__lowerCamelCase    ,       forced_eos_token_id=__lowerCamelCase    ,       **__lowerCamelCase    ,       )






class 					lowercase_			(	lowerCAmelCase_       ):





     @property
     def  _lowerCAmelCase			(    self    :				Dict ):
           if self.task in ["default", "seq2seq-lm"]:
                 snake_case__					:      Optional[Any]  			=						OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ] )

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

                 if self.use_past:
                       self.fill_with_past_key_values_(__lowerCamelCase    ,       direction='inputs' )
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 snake_case__					:      Optional[int]  			=						OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ] )
                 if self.use_past:
                       snake_case__			,       snake_case__					:      Tuple  			=						self.num_layers
                       for i in range(__lowerCamelCase ):
                             snake_case__					:      Optional[int]  			=						{0: 'batch', 2: 'past_sequence + sequence'}
                             snake_case__					:      Optional[Any]  			=						{0: 'batch', 2: 'past_sequence + sequence'}
           else:
                 snake_case__					:      Union[str, Any]  			=						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  _lowerCAmelCase			(    self    :				List[str] ):
           if self.task in ["default", "seq2seq-lm"]:
                 snake_case__					:      Any  			=						super().outputs
           else:
                 snake_case__					:      Optional[int]  			=						super(__lowerCamelCase    ,       self ).outputs
                 if self.use_past:
                       snake_case__			,       snake_case__					:      Optional[int]  			=						self.num_layers
                       for i in range(__lowerCamelCase ):
                             snake_case__					:      Tuple  			=						{0: 'batch', 2: 'past_sequence + sequence'}
                             snake_case__					:      Dict  			=						{0: 'batch', 2: 'past_sequence + sequence'}
           return common_outputs





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				PreTrainedTokenizer    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				bool = False    ,       __lowerCamelCase    :				Optional[TensorType] = None    ,       ):
           snake_case__					:      List[str]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )

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

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

                 snake_case__					:      Dict  			=						torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(__lowerCamelCase    ,       __lowerCamelCase )]    ,       dim=1 )

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

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





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				PreTrainedTokenizer    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				bool = False    ,       __lowerCamelCase    :				Optional[TensorType] = None    ,       ):
           snake_case__					:      Tuple  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )

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

                 snake_case__					:      Union[str, Any]  			=						common_inputs['attention_mask'].dtype
                 snake_case__					:      Optional[int]  			=						torch.cat(
                     [common_inputs['attention_mask'], torch.ones(__lowerCamelCase    ,       __lowerCamelCase    ,       dtype=__lowerCamelCase )]    ,       dim=1 )
                 snake_case__					:      str  			=						[
                     (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                 ]
           return common_inputs





     def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				PreTrainedTokenizer    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				bool = False    ,       __lowerCamelCase    :				Optional[TensorType] = None    ,       ):
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           snake_case__					:      str  			=						compute_effective_axis_dimension(
               __lowerCamelCase    ,       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
           snake_case__					:      Union[str, Any]  			=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
           snake_case__					:      List[Any]  			=						compute_effective_axis_dimension(
               __lowerCamelCase    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=__lowerCamelCase )

           # Generate dummy inputs according to compute batch and sequence
           snake_case__					:      Optional[int]  			=						[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
           snake_case__					:      Tuple  			=						dict(tokenizer(__lowerCamelCase    ,       return_tensors=__lowerCamelCase ) )
           return common_inputs





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				PreTrainedTokenizer    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				int = -1    ,       __lowerCamelCase    :				bool = False    ,       __lowerCamelCase    :				Optional[TensorType] = None    ,       ):
           if self.task in ["default", "seq2seq-lm"]:
                 snake_case__					:      Optional[Any]  			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     __lowerCamelCase    ,       batch_size=__lowerCamelCase    ,       seq_length=__lowerCamelCase    ,       is_pair=__lowerCamelCase    ,       framework=__lowerCamelCase )

           elif self.task == "causal-lm":
                 snake_case__					:      Optional[int]  			=						self._generate_dummy_inputs_for_causal_lm(
                     __lowerCamelCase    ,       batch_size=__lowerCamelCase    ,       seq_length=__lowerCamelCase    ,       is_pair=__lowerCamelCase    ,       framework=__lowerCamelCase )
           else:
                 snake_case__					:      Union[str, Any]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     __lowerCamelCase    ,       batch_size=__lowerCamelCase    ,       seq_length=__lowerCamelCase    ,       is_pair=__lowerCamelCase    ,       framework=__lowerCamelCase )

           return common_inputs





     def  _lowerCAmelCase			(    self    :				str    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				int ):
           if self.task in ["default", "seq2seq-lm"]:
                 snake_case__					:      Optional[int]  			=						super()._flatten_past_key_values_(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           else:
                 snake_case__					:      str  			=						super(__lowerCamelCase    ,       self )._flatten_past_key_values_(
                     __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )






'''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 ViltImageProcessor






class 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				Optional[Any]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any]=7    ,       __lowerCamelCase    :				Optional[Any]=3    ,       __lowerCamelCase    :				int=18    ,       __lowerCamelCase    :				List[str]=30    ,       __lowerCamelCase    :				List[str]=400    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				List[str]=2    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				List[Any]=[0.5, 0.5, 0.5]    ,       __lowerCamelCase    :				Any=[0.5, 0.5, 0.5]    ,       ):
           snake_case__					:      Dict  			=						size if size is not None else {'shortest_edge': 30}
           snake_case__					:      Any  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      List[str]  			=						num_channels
           snake_case__					:      str  			=						image_size
           snake_case__					:      List[Any]  			=						min_resolution
           snake_case__					:      Dict  			=						max_resolution
           snake_case__					:      str  			=						do_resize
           snake_case__					:      str  			=						size
           snake_case__					:      List[str]  			=						size_divisor
           snake_case__					:      List[Any]  			=						do_normalize
           snake_case__					:      Optional[Any]  			=						image_mean
           snake_case__					:      Union[str, Any]  			=						image_std





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any]=False ):
           if not batched:
                 snake_case__					:      int  			=						self.size['shortest_edge']
                 snake_case__					:      Union[str, Any]  			=						image_inputs[0]
                 if isinstance(__lowerCamelCase    ,       Image.Image ):
                       snake_case__			,       snake_case__					:      Any  			=						image.size
                 else:
                       snake_case__			,       snake_case__					:      Optional[Any]  			=						image.shape[1], image.shape[2]
                 snake_case__					:      int  			=						size / min(__lowerCamelCase    ,       __lowerCamelCase )
                 if h < w:
                       snake_case__			,       snake_case__					:      Tuple  			=						size, scale * w
                 else:
                       snake_case__			,       snake_case__					:      List[str]  			=						scale * h, size

                 snake_case__					:      List[Any]  			=						int((1333 / 800) * size )
                 if max(__lowerCamelCase    ,       __lowerCamelCase ) > max_size:
                       snake_case__					:      Dict  			=						max_size / max(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[Any]  			=						newh * scale
                       snake_case__					:      Optional[Any]  			=						neww * scale

                 snake_case__			,       snake_case__					:      str  			=						int(newh + 0.5 ), int(neww + 0.5 )
                 snake_case__			,       snake_case__					:      Any  			=						(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 snake_case__					:      int  			=						[]
                 for image in image_inputs:
                       snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 snake_case__					:      Any  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[0] )[0]
                 snake_case__					:      List[Any]  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[1] )[1]

           return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= ViltImageProcessor if is_vision_available() else None





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      int  			=						ViltImageProcessingTester(self )





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.image_processor_tester.prepare_image_processor_dict()





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_mean' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_std' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_normalize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_resize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size_divisor' ) )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 30} )

           snake_case__					:      List[str]  			=						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42 )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 42} )





     def  _lowerCAmelCase			(    self    :				str ):
           pass





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           # Initialize image_processing
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           snake_case__					:      str  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       Image.Image )

           # Test not batched input
           snake_case__					:      int  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           # Initialize image_processing
           snake_case__					:      List[Any]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       numpify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       np.ndarray )

           # Test not batched input
           snake_case__					:      List[Any]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				str ):
           # Initialize image_processing
           snake_case__					:      str  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       torchify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       torch.Tensor )

           # Test not batched input
           snake_case__					:      Optional[int]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[int]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Optional[int]  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )
