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 		_lowercase						(					snake_case_       ):
 lowercase				     =   ['pixel_values']

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

  """simple docstring"""





  super().__init__(**snake_case			)
  UpperCamelCase_					:						Any =					size if size is not None else {'shortest_edge': 2_2_4}
  UpperCamelCase_					:						Optional[int] =					get_size_dict(snake_case   ,      default_to_square=snake_case			)
  UpperCamelCase_					:						Dict =					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
  UpperCamelCase_					:						int =					get_size_dict(snake_case   ,      default_to_square=snake_case   ,      param_name='crop_size'			)

  UpperCamelCase_					:						Tuple =					do_resize
  UpperCamelCase_					:						Any =					size
  UpperCamelCase_					:						Optional[Any] =					resample
  UpperCamelCase_					:						Dict =					do_center_crop
  UpperCamelCase_					:						Dict =					crop_size
  UpperCamelCase_					:						str =					do_rescale
  UpperCamelCase_					:						Dict =					rescale_factor
  UpperCamelCase_					:						Tuple =					do_normalize
  UpperCamelCase_					:						Any =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  UpperCamelCase_					:						Optional[Any] =					image_std if image_std is not None else OPENAI_CLIP_STD
  UpperCamelCase_					:						Union[str, Any] =					do_convert_rgb

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							np.ndarray   ,      snake_case	:							Dict[str, int]   ,      snake_case	:							PILImageResampling = PILImageResampling.BICUBIC   ,      snake_case	:							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case	:							Union[str, Any]   ,      )   ->						np.ndarray:

  """simple docstring"""





  UpperCamelCase_					:						Any =					get_size_dict(snake_case   ,      default_to_square=snake_case			)
  if "shortest_edge" not in size:
   raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
  UpperCamelCase_					:						List[str] =					get_resize_output_image_size(snake_case   ,      size=size['shortest_edge']   ,      default_to_square=snake_case			)
  return resize(snake_case   ,      size=snake_case   ,      resample=snake_case   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							np.ndarray   ,      snake_case	:							Dict[str, int]   ,      snake_case	:							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case	:							List[str]   ,      )   ->						np.ndarray:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					get_size_dict(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(snake_case   ,      size=(size['height'], size['width'])   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							np.ndarray   ,      snake_case	:							Union[int, float]   ,      snake_case	:							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case	:							List[str]   ,      )   ->						Dict:

  """simple docstring"""





  return rescale(snake_case   ,      scale=snake_case   ,      data_format=snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							np.ndarray   ,      snake_case	:							Union[float, List[float]]   ,      snake_case	:							Union[float, List[float]]   ,      snake_case	:							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case	:							List[Any]   ,      )   ->						np.ndarray:

  """simple docstring"""





  return normalize(snake_case   ,      mean=snake_case   ,      std=snake_case   ,      data_format=snake_case   ,      **snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							ImageInput   ,      snake_case	:							bool = None   ,      snake_case	:							Dict[str, int] = None   ,      snake_case	:							PILImageResampling = None   ,      snake_case	:							bool = None   ,      snake_case	:							int = None   ,      snake_case	:							bool = None   ,      snake_case	:							float = None   ,      snake_case	:							bool = None   ,      snake_case	:							Optional[Union[float, List[float]]] = None   ,      snake_case	:							Optional[Union[float, List[float]]] = None   ,      snake_case	:							bool = None   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      snake_case	:							Optional[ChannelDimension] = ChannelDimension.FIRST   ,      **snake_case	:							str   ,      )   ->						PIL.Image.Image:

  """simple docstring"""





  UpperCamelCase_					:						Any =					do_resize if do_resize is not None else self.do_resize
  UpperCamelCase_					:						Union[str, Any] =					size if size is not None else self.size
  UpperCamelCase_					:						Any =					get_size_dict(snake_case   ,      param_name='size'   ,      default_to_square=snake_case			)
  UpperCamelCase_					:						Optional[Any] =					resample if resample is not None else self.resample
  UpperCamelCase_					:						Optional[Any] =					do_center_crop if do_center_crop is not None else self.do_center_crop
  UpperCamelCase_					:						str =					crop_size if crop_size is not None else self.crop_size
  UpperCamelCase_					:						Union[str, Any] =					get_size_dict(snake_case   ,      param_name='crop_size'   ,      default_to_square=snake_case			)
  UpperCamelCase_					:						Any =					do_rescale if do_rescale is not None else self.do_rescale
  UpperCamelCase_					:						Union[str, Any] =					rescale_factor if rescale_factor is not None else self.rescale_factor
  UpperCamelCase_					:						List[str] =					do_normalize if do_normalize is not None else self.do_normalize
  UpperCamelCase_					:						List[Any] =					image_mean if image_mean is not None else self.image_mean
  UpperCamelCase_					:						List[Any] =					image_std if image_std is not None else self.image_std
  UpperCamelCase_					:						str =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

  UpperCamelCase_					:						Dict =					make_list_of_images(snake_case			)

  if not valid_images(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:
   UpperCamelCase_					:						Any =					[convert_to_rgb(snake_case			) for image in images]

  # All transformations expect numpy arrays.
  UpperCamelCase_					:						Tuple =					[to_numpy_array(snake_case			) for image in images]

  if do_resize:
   UpperCamelCase_					:						str =					[self.resize(image=snake_case   ,      size=snake_case   ,      resample=snake_case			) for image in images]

  if do_center_crop:
   UpperCamelCase_					:						Any =					[self.center_crop(image=snake_case   ,      size=snake_case			) for image in images]

  if do_rescale:
   UpperCamelCase_					:						str =					[self.rescale(image=snake_case   ,      scale=snake_case			) for image in images]

  if do_normalize:
   UpperCamelCase_					:						Any =					[self.normalize(image=snake_case   ,      mean=snake_case   ,      std=snake_case			) for image in images]

  UpperCamelCase_					:						List[Any] =					[to_channel_dimension_format(snake_case   ,      snake_case			) for image in images]

  UpperCamelCase_					:						Dict =					{'pixel_values': images}
  return BatchFeature(data=snake_case   ,      tensor_type=snake_case			)




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



