

'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   def __init__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      float     ,**SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:Tuple						      =					feature_size
     SCREAMING_SNAKE_CASE:Dict						      =					sampling_rate
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					padding_value

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					kwargs.pop("padding_side"     ,"right"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					kwargs.pop("return_attention_mask"     ,SCREAMING_SNAKE_CASE__							)

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[
           BatchFeature,
           List[BatchFeature],
           Dict[str, BatchFeature],
           Dict[str, List[BatchFeature]],
           List[Dict[str, BatchFeature]],
       ]     ,SCREAMING_SNAKE_CASE__			:      Union[bool, str, PaddingStrategy] = True     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[bool] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,):
     # If we have a list of dicts, let's convert it in a dict of lists
     # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
     if isinstance(SCREAMING_SNAKE_CASE__     ,(list, tuple)							) and isinstance(processed_features[0]     ,(dict, BatchFeature)							):
       SCREAMING_SNAKE_CASE:Any						      =					{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

     # The model's main input name, usually `input_values`, has be passed for padding
     if self.model_input_names[0] not in processed_features:
       raise ValueError(
           "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
           F''' to this method that includes {self.model_input_names[0]}, but you provided'''
           F''' {list(processed_features.keys()							)}'''							)

     SCREAMING_SNAKE_CASE:str						      =					processed_features[self.model_input_names[0]]
     SCREAMING_SNAKE_CASE:Optional[int]						      =					(
         return_attention_mask if return_attention_mask is not None else self.return_attention_mask
     )

     if len(SCREAMING_SNAKE_CASE__							) == 0:
       if return_attention_mask:
         SCREAMING_SNAKE_CASE:Any						      =					[]
       return processed_features

     # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
     # and rebuild them afterwards if no return_tensors is specified
     # Note that we lose the specific device the tensor may be on for PyTorch

     SCREAMING_SNAKE_CASE:Optional[int]						      =					required_input[0]
     if isinstance(SCREAMING_SNAKE_CASE__     ,(list, tuple)							):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0
       while len(required_input[index]							) == 0:
         index += 1
       if index < len(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:List[Any]						      =					required_input[index][0]

     if return_tensors is None:
       if is_tf_tensor(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:str						      =					"tf"
       elif is_torch_tensor(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:List[str]						      =					"pt"
       elif isinstance(SCREAMING_SNAKE_CASE__     ,(int, float, list, tuple, np.ndarray)							):
         SCREAMING_SNAKE_CASE:Any						      =					"np"
       else:
         raise ValueError(
             F'''type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE__							)}. '''
             "Should be one of a python, numpy, pytorch or tensorflow object."							)

     for key, value in processed_features.items():
       if isinstance(value[0]     ,(int, float)							):
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					to_numpy(SCREAMING_SNAKE_CASE__							)
       else:
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[to_numpy(SCREAMING_SNAKE_CASE__							) for v in value]

        # Convert padding_strategy in PaddingStrategy
     SCREAMING_SNAKE_CASE:Dict						      =					self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__     ,max_length=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					processed_features[self.model_input_names[0]]

     SCREAMING_SNAKE_CASE:Tuple						      =					len(SCREAMING_SNAKE_CASE__							)
     if not all(len(SCREAMING_SNAKE_CASE__							) == batch_size for v in processed_features.values()							):
       raise ValueError("Some items in the output dictionary have a different batch size than others."							)

     SCREAMING_SNAKE_CASE:List[str]						      =					[]
     for i in range(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Dict						      =					{k: v[i] for k, v in processed_features.items()}
       # truncation
       SCREAMING_SNAKE_CASE:List[Any]						      =					self._truncate(
           SCREAMING_SNAKE_CASE__     ,max_length=SCREAMING_SNAKE_CASE__     ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__     ,truncation=SCREAMING_SNAKE_CASE__     ,)
       truncated_inputs.append(SCREAMING_SNAKE_CASE__							)

     if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       SCREAMING_SNAKE_CASE:Tuple						      =					max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
       SCREAMING_SNAKE_CASE:int						      =					PaddingStrategy.MAX_LENGTH

     SCREAMING_SNAKE_CASE:Dict						      =					{}
     for i in range(SCREAMING_SNAKE_CASE__							):
       # padding
       SCREAMING_SNAKE_CASE:List[Any]						      =					self._pad(
           truncated_inputs[i]     ,max_length=SCREAMING_SNAKE_CASE__     ,padding_strategy=SCREAMING_SNAKE_CASE__     ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__     ,return_attention_mask=SCREAMING_SNAKE_CASE__     ,)

       for key, value in outputs.items():
         if key not in batch_outputs:
           SCREAMING_SNAKE_CASE:Dict						      =					[]
         if value.dtype is np.dtype(np.floataa							):
           SCREAMING_SNAKE_CASE:List[str]						      =					value.astype(np.floataa							)
         batch_outputs[key].append(SCREAMING_SNAKE_CASE__							)

     return BatchFeature(SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Union[Dict[str, np.ndarray], BatchFeature]     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[bool] = None     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					processed_features[self.model_input_names[0]]

     if padding_strategy == PaddingStrategy.LONGEST:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					len(SCREAMING_SNAKE_CASE__							)

     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       SCREAMING_SNAKE_CASE:List[Any]						      =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     SCREAMING_SNAKE_CASE:Tuple						      =					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__							) < max_length

     if return_attention_mask and "attention_mask" not in processed_features:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.ones(len(SCREAMING_SNAKE_CASE__							)     ,dtype=np.intaa							)

     if needs_to_be_padded:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					max_length - len(SCREAMING_SNAKE_CASE__							)
       if self.padding_side == "right":
         if return_attention_mask:
           SCREAMING_SNAKE_CASE:Tuple						      =					np.pad(
               processed_features["attention_mask"]     ,(0, difference)							)
         SCREAMING_SNAKE_CASE:Dict						      =					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
         SCREAMING_SNAKE_CASE:int						      =					np.pad(
             SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,"constant"     ,constant_values=self.padding_value							)
       elif self.padding_side == "left":
         if return_attention_mask:
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.pad(
               processed_features["attention_mask"]     ,(difference, 0)							)
         SCREAMING_SNAKE_CASE:Any						      =					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
         SCREAMING_SNAKE_CASE:str						      =					np.pad(
             SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,"constant"     ,constant_values=self.padding_value							)
       else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

     return processed_features
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[Dict[str, np.ndarray], BatchFeature]     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[bool] = None     ,):
     if not truncation:
       return processed_features
     elif truncation and max_length is None:
       raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					processed_features[self.model_input_names[0]]

     # find `max_length` that fits `pad_to_multiple_of`
     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					len(SCREAMING_SNAKE_CASE__							) > max_length

     if needs_to_be_truncated:
       SCREAMING_SNAKE_CASE:List[Any]						      =					processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
         SCREAMING_SNAKE_CASE:Any						      =					processed_features["attention_mask"][:max_length]

     return processed_features







   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=False     ,SCREAMING_SNAKE_CASE__			:      str=None							):

     # Get padding strategy
     if padding is not False:
       if padding is True:
         SCREAMING_SNAKE_CASE:Tuple						      =					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:Any						      =					PaddingStrategy(SCREAMING_SNAKE_CASE__							)
       elif isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:Dict						      =					padding
     else:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					PaddingStrategy.DO_NOT_PAD

     # Set max length if needed
     if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
         raise ValueError(
             F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''							)

        # Test if we have a padding value
     if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
           " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."							)

     return padding_strategy









'''simple docstring'''

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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_       						=							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case (       _a						):
   _A       :     int       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:int						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__     ,param_name="crop_size"							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size
     SCREAMING_SNAKE_CASE:List[Any]						      =					resample
     SCREAMING_SNAKE_CASE:Any						      =					do_center_crop
     SCREAMING_SNAKE_CASE:List[Any]						      =					crop_size
     SCREAMING_SNAKE_CASE:Tuple						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Dict						      =					do_normalize
     SCREAMING_SNAKE_CASE:int						      =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_convert_rgb
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Any     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=size["shortest_edge"]     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__							)
     if "height" not in size or "width" not in size:
       raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
     return center_crop(SCREAMING_SNAKE_CASE__     ,size=(size["height"], size["width"])     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Tuple     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[ChannelDimension] = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     SCREAMING_SNAKE_CASE:Any						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Dict						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Optional[int]						      =					crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="crop_size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:Tuple						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:List[Any]						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:List[str]						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Any						      =					image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE:str						      =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE:Any						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       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_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True."							)

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

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       SCREAMING_SNAKE_CASE:List[Any]						      =					[convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:Optional[int]						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.center_crop(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Any						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[str]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:List[Any]						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






