


'''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 convert_to_rgb, 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


if is_vision_available():
       import PIL


a_					:    List[str]          =    logging.get_logger(__name__)




class  a     (     UpperCAmelCase__       ):
 _lowerCAmelCase 					=	["pixel_values"]
 def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = PILImageResampling.BICUBIC      ,				__magic_name__ = True      ,				__magic_name__ = 1 / 2_55      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = True      ,				**__magic_name__      ,				)					->							None:
      super().__init__(**lowerCamelCase__						)
      _a			      =							size if size is not None else {"height": 3_84, "width": 3_84}
      _a			      =							get_size_dict(lowerCamelCase__      ,				default_to_square=lowerCamelCase__						)

      _a			      =							do_resize
      _a			      =							size
      _a			      =							resample
      _a			      =							do_rescale
      _a			      =							rescale_factor
      _a			      =							do_normalize
      _a			      =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      _a			      =							image_std if image_std is not None else OPENAI_CLIP_STD
      _a			      =							do_convert_rgb
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = PILImageResampling.BICUBIC      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(lowerCamelCase__      ,				default_to_square=lowerCamelCase__						)
      if "height" not in size or "width" not in size:
           raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'						)
      _a			      =							(size["height"], size["width"])
      return resize(lowerCamelCase__      ,				size=lowerCamelCase__      ,				resample=lowerCamelCase__      ,				data_format=lowerCamelCase__      ,				**lowerCamelCase__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							str:
      return rescale(lowerCamelCase__      ,				scale=lowerCamelCase__      ,				data_format=lowerCamelCase__      ,				**lowerCamelCase__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      return normalize(lowerCamelCase__      ,				mean=lowerCamelCase__      ,				std=lowerCamelCase__      ,				data_format=lowerCamelCase__      ,				**lowerCamelCase__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							PIL.Image.Image:
      _a			      =							do_resize if do_resize is not None else self.do_resize
      _a			      =							resample if resample is not None else self.resample
      _a			      =							do_rescale if do_rescale is not None else self.do_rescale
      _a			      =							rescale_factor if rescale_factor is not None else self.rescale_factor
      _a			      =							do_normalize if do_normalize is not None else self.do_normalize
      _a			      =							image_mean if image_mean is not None else self.image_mean
      _a			      =							image_std if image_std is not None else self.image_std
      _a			      =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      _a			      =							size if size is not None else self.size
      _a			      =							get_size_dict(lowerCamelCase__      ,				default_to_square=lowerCamelCase__						)

      _a			      =							make_list_of_images(lowerCamelCase__						)

      if not valid_images(lowerCamelCase__						):
           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 or resample is None:
           raise ValueError('Size and resample 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_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:
           _a			      =							[convert_to_rgb(lowerCamelCase__						) for image in images]

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

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

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

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

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

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

      return encoded_outputs





'''simple docstring'''






from typing import Any






def 	_A				(lowerCAmelCase__  :list		)   ->  list[Any]:




     '''simple docstring'''



     if not input_list:
          return []
     _a			      =							[input_list.count(lowerCAmelCase__		) for value in input_list]
     _a			      =							max(lowerCAmelCase__		)  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(lowerCAmelCase__		) if value == y}		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

