




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


__SCREAMING_SNAKE_CASE    :   int       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   List[str]       ={
    """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 			A			(		__lowerCamelCase		):
     """simple docstring"""

     _snake_case:	Dict     ="mbart"
     _snake_case:	Tuple     =["past_key_values"]
     _snake_case:	Optional[int]     ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




     def __init__(    self  :   Optional[Any]	,  A__  :   Any=5_0_2_6_5	,  A__  :   Any=1_0_2_4	,  A__  :   Optional[Any]=1_2	,  A__  :   int=4_0_9_6	,  A__  :   Optional[int]=1_6	,  A__  :   List[str]=1_2	,  A__  :   int=4_0_9_6	,  A__  :   Any=1_6	,  A__  :   Dict=0.0	,  A__  :   Optional[Any]=0.0	,  A__  :   Optional[Any]=True	,  A__  :   str=True	,  A__  :   List[Any]="gelu"	,  A__  :   int=1_0_2_4	,  A__  :   Optional[Any]=0.1	,  A__  :   Any=0.0	,  A__  :   List[Any]=0.0	,  A__  :   Any=0.02	,  A__  :   int=0.0	,  A__  :   Dict=False	,  A__  :   Optional[Any]=1	,  A__  :   int=0	,  A__  :   Tuple=2	,  A__  :   Any=2	,  **A__  :   List[Any]	,  )				->       Tuple:
           lowerCamelCase__        =				vocab_size
           lowerCamelCase__        =				max_position_embeddings
           lowerCamelCase__        =				d_model
           lowerCamelCase__        =				encoder_ffn_dim
           lowerCamelCase__        =				encoder_layers
           lowerCamelCase__        =				encoder_attention_heads
           lowerCamelCase__        =				decoder_ffn_dim
           lowerCamelCase__        =				decoder_layers
           lowerCamelCase__        =				decoder_attention_heads
           lowerCamelCase__        =				dropout
           lowerCamelCase__        =				attention_dropout
           lowerCamelCase__        =				activation_dropout
           lowerCamelCase__        =				activation_function
           lowerCamelCase__        =				init_std
           lowerCamelCase__        =				encoder_layerdrop
           lowerCamelCase__        =				decoder_layerdrop
           lowerCamelCase__        =				classifier_dropout
           lowerCamelCase__        =				use_cache
           lowerCamelCase__        =				encoder_layers
           lowerCamelCase__        =				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 			A			(		__lowerCamelCase		):
     """simple docstring"""

     @property
     def  __snake_case   (    self  :   str			)				->       str:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__        =				OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]			)

                 if self.use_past:
                       lowerCamelCase__        =				{0: """batch"""}
                       lowerCamelCase__        =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                 else:
                       lowerCamelCase__        =				{0: """batch""", 1: """decoder_sequence"""}
                       lowerCamelCase__        =				{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.
                 lowerCamelCase__        =				OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]			)
                 if self.use_past:
                       lowerCamelCase__        =				self.num_layers
                       for i in range(_lowerCAmelCase			):
                             lowerCamelCase__        =				{0: """batch""", 2: """past_sequence + sequence"""}
                             lowerCamelCase__        =				{0: """batch""", 2: """past_sequence + sequence"""}
           else:
                 lowerCamelCase__        =				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  __snake_case   (    self  :   Tuple			)				->       List[Any]:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__        =				super().outputs
           else:
                 lowerCamelCase__        =				super(_lowerCAmelCase	,  self			).outputs
                 if self.use_past:
                       lowerCamelCase__        =				self.num_layers
                       for i in range(_lowerCAmelCase			):
                             lowerCamelCase__        =				{0: """batch""", 2: """past_sequence + sequence"""}
                             lowerCamelCase__        =				{0: """batch""", 2: """past_sequence + sequence"""}
           return common_outputs




     def  __snake_case   (    self  :   Any	,  A__  :   Optional[Any]	,  A__  :   Any = -1	,  A__  :   str = -1	,  A__  :   Dict = False	,  A__  :   List[str] = None	,  )				->       List[Any]:
           lowerCamelCase__        =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase			)

           # Generate decoder inputs
           lowerCamelCase__        =				seq_length if not self.use_past else 1
           lowerCamelCase__        =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase			)
           lowerCamelCase__        =				{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           lowerCamelCase__        =				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
                 lowerCamelCase__        =				common_inputs["""input_ids"""].shape
                 lowerCamelCase__        =				common_inputs["""decoder_input_ids"""].shape[1]
                 lowerCamelCase__        =				self.num_attention_heads
                 lowerCamelCase__        =				(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 lowerCamelCase__        =				decoder_seq_length + 3
                 lowerCamelCase__        =				(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 lowerCamelCase__        =				torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(_lowerCAmelCase	,  _lowerCAmelCase			)]	,  dim=1			)

                 lowerCamelCase__        =				[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 lowerCamelCase__        =				self.num_layers
                 lowerCamelCase__        =				min(_lowerCAmelCase	,  _lowerCAmelCase			)
                 lowerCamelCase__        =				max(_lowerCAmelCase	,  _lowerCAmelCase			) - min_num_layers
                 lowerCamelCase__        =				"""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.
                 lowerCamelCase__        =				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  __snake_case   (    self  :   Optional[Any]	,  A__  :   str	,  A__  :   List[str] = -1	,  A__  :   Any = -1	,  A__  :   Dict = False	,  A__  :   Any = None	,  )				->       Dict:
           lowerCamelCase__        =				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
                 lowerCamelCase__        =				common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 lowerCamelCase__        =				seqlen + 2
                 lowerCamelCase__        =				self.num_layers
                 lowerCamelCase__        =				self.num_attention_heads
                 lowerCamelCase__        =				(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 lowerCamelCase__        =				common_inputs["""attention_mask"""].dtype
                 lowerCamelCase__        =				torch.cat(
                     [common_inputs['attention_mask'], torch.ones(_lowerCAmelCase	,  _lowerCAmelCase	,  dtype=_lowerCAmelCase			)]	,  dim=1			)
                 lowerCamelCase__        =				[
                     (torch.zeros(_lowerCAmelCase			), torch.zeros(_lowerCAmelCase			)) for _ in range(_lowerCAmelCase			)
                 ]
           return common_inputs




     def  __snake_case   (    self  :   Dict	,  A__  :   Tuple	,  A__  :   int = -1	,  A__  :   Union[str, Any] = -1	,  A__  :   Union[str, Any] = False	,  A__  :   int = None	,  )				->       str:
           # 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
           lowerCamelCase__        =				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
           lowerCamelCase__        =				tokenizer.num_special_tokens_to_add(_lowerCAmelCase			)
           lowerCamelCase__        =				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
           lowerCamelCase__        =				[""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
           lowerCamelCase__        =				dict(tokenizer(_lowerCAmelCase	,  return_tensors=_lowerCAmelCase			)			)
           return common_inputs




     def  __snake_case   (    self  :   List[str]	,  A__  :   Tuple	,  A__  :   Union[str, Any] = -1	,  A__  :   Optional[Any] = -1	,  A__  :   Any = False	,  A__  :   Optional[Any] = None	,  )				->       str:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__        =				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":
                 lowerCamelCase__        =				self._generate_dummy_inputs_for_causal_lm(
                     _lowerCAmelCase	,  batch_size=_lowerCAmelCase	,  seq_length=_lowerCAmelCase	,  is_pair=_lowerCAmelCase	,  framework=_lowerCAmelCase			)
           else:
                 lowerCamelCase__        =				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  __snake_case   (    self  :   List[Any]	,  A__  :   Union[str, Any]	,  A__  :   int	,  A__  :   List[str]	,  A__  :   Optional[Any]			)				->       str:
           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__        =				super()._flatten_past_key_values_(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase			)
           else:
                 lowerCamelCase__        =				super(_lowerCAmelCase	,  self			)._flatten_past_key_values_(
                     _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase			)





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["pixel_values"]




     def __init__(    self  :   Optional[Any]	,  A__  :   bool = True	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = PILImageResampling.BILINEAR	,  A__  :   bool = True	,  A__  :   bool = False	,  A__  :   bool = True	,  A__  :   bool = True	,  A__  :   Union[int, float] = 1 / 2_5_5	,  A__  :   bool = True	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  **A__  :   List[str]	,  )				->       None:
           super().__init__(**A__			)

           lowerCamelCase__        =				size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
           if isinstance(A__	,  (tuple, list)			):
                 # The previous feature extractor size parameter was in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)

           lowerCamelCase__        =				do_resize
           lowerCamelCase__        =				size
           lowerCamelCase__        =				resample
           lowerCamelCase__        =				do_thumbnail
           lowerCamelCase__        =				do_align_long_axis
           lowerCamelCase__        =				do_pad
           lowerCamelCase__        =				do_rescale
           lowerCamelCase__        =				rescale_factor
           lowerCamelCase__        =				do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase__        =				image_std if image_std is not None else IMAGENET_STANDARD_STD




     def  __snake_case   (    self  :   str	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   Optional[Union[str, ChannelDimension]] = None			)				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           if (output_width < output_height and input_width > input_height) or (
               output_width > output_height and input_width < input_height
           ):
                 lowerCamelCase__        =				np.rotaa(A__	,  3			)

           if data_format is not None:
                 lowerCamelCase__        =				to_channel_dimension_format(A__	,  A__			)

           return image




     def  __snake_case   (    self  :   Tuple	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   bool = False	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)

           lowerCamelCase__        =				output_width - input_width
           lowerCamelCase__        =				output_height - input_height

           if random_padding:
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_height + 1			)
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_width + 1			)
           else:
                 lowerCamelCase__        =				delta_height // 2
                 lowerCamelCase__        =				delta_width // 2

           lowerCamelCase__        =				delta_height - pad_top
           lowerCamelCase__        =				delta_width - pad_left

           lowerCamelCase__        =				((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(A__	,  A__	,  data_format=A__			)




     def  __snake_case   (    self  :   int	,  *A__  :   Tuple	,  **A__  :   Dict			)				->       List[Any]:
           logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
           return self.pad_image(*A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           # We always resize to the smallest of either the input or output size.
           lowerCamelCase__        =				min(A__	,  A__			)
           lowerCamelCase__        =				min(A__	,  A__			)

           if height == input_height and width == input_width:
                 return image

           if input_height > input_width:
                 lowerCamelCase__        =				int(input_width * height / input_height			)
           elif input_width > input_height:
                 lowerCamelCase__        =				int(input_height * width / input_width			)

           return resize(
               A__	,  size=(height, width)	,  resample=A__	,  reducing_gap=2.0	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   List[str]	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				min(size['height']	,  size['width']			)
           lowerCamelCase__        =				get_resize_output_image_size(A__	,  size=A__	,  default_to_square=A__			)
           lowerCamelCase__        =				resize(A__	,  size=A__	,  resample=A__	,  data_format=A__	,  **A__			)
           return resized_image




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   np.ndarray	,  A__  :   Union[int, float]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Optional[int]	,  )				->       Tuple:
           return rescale(A__	,  scale=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Union[float, List[float]]	,  A__  :   Union[float, List[float]]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Union[str, Any]	,  )				->       np.ndarray:
           return normalize(A__	,  mean=A__	,  std=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   ImageInput	,  A__  :   bool = None	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = False	,  A__  :   bool = None	,  A__  :   float = None	,  A__  :   bool = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[str, TensorType]] = None	,  A__  :   Optional[ChannelDimension] = ChannelDimension.FIRST	,  **A__  :   Any	,  )				->       PIL.Image.Image:
           lowerCamelCase__        =				do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__        =				size if size is not None else self.size
           if isinstance(A__	,  (tuple, list)			):
                 # Previous feature extractor had size in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				resample if resample is not None else self.resample
           lowerCamelCase__        =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           lowerCamelCase__        =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           lowerCamelCase__        =				do_pad if do_pad is not None else self.do_pad
           lowerCamelCase__        =				do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__        =				rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__        =				do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__        =				image_std if image_std is not None else self.image_std

           lowerCamelCase__        =				make_list_of_images(A__			)

           if not valid_images(A__			):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'			)

           if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'			)

           if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

           if do_pad and size is None:
                 raise ValueError('Size must be specified if do_pad is True.'			)

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

           # All transformations expect numpy arrays.
           lowerCamelCase__        =				[to_numpy_array(A__			) for image in images]

           if do_align_long_axis:
                 lowerCamelCase__        =				[self.align_long_axis(A__	,  size=A__			) for image in images]

           if do_resize:
                 lowerCamelCase__        =				[self.resize(image=A__	,  size=A__	,  resample=A__			) for image in images]

           if do_thumbnail:
                 lowerCamelCase__        =				[self.thumbnail(image=A__	,  size=A__			) for image in images]

           if do_pad:
                 lowerCamelCase__        =				[self.pad_image(image=A__	,  size=A__	,  random_padding=A__			) for image in images]

           if do_rescale:
                 lowerCamelCase__        =				[self.rescale(image=A__	,  scale=A__			) for image in images]

           if do_normalize:
                 lowerCamelCase__        =				[self.normalize(image=A__	,  mean=A__	,  std=A__			) for image in images]

           lowerCamelCase__        =				[to_channel_dimension_format(A__	,  A__			) for image in images]

           lowerCamelCase__        =				{'pixel_values': images}
           return BatchFeature(data=A__	,  tensor_type=A__			)