

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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a						:Union[str, Any]            = logging.get_logger(__name__)

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: List[Any]					=					['pixel_values']





      def __init__(      self       :							Tuple	, UpperCAmelCase       :							bool = True	, UpperCAmelCase       :							Optional[Dict[str, int]] = None	, UpperCAmelCase       :							PILImageResampling = PILImageResampling.BILINEAR	, UpperCAmelCase       :							bool = True	, UpperCAmelCase       :							Union[int, float] = 1 / 255	, UpperCAmelCase       :							bool = True	, UpperCAmelCase       :							Optional[Union[float, List[float]]] = None	, UpperCAmelCase       :							Optional[Union[float, List[float]]] = None	, **UpperCAmelCase       :							Optional[int]	, ):
            super().__init__(**UpperCAmelCase  )
            A_	     =							size if size is not None else {"height": 224, "width": 224}
            A_	     =							get_size_dict(UpperCAmelCase  )
            A_	     =							do_resize
            A_	     =							do_rescale
            A_	     =							do_normalize
            A_	     =							size
            A_	     =							resample
            A_	     =							rescale_factor
            A_	     =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            A_	     =							image_std if image_std is not None else IMAGENET_STANDARD_STD





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							np.ndarray	, UpperCAmelCase       :							Dict[str, int]	, UpperCAmelCase       :							PILImageResampling = PILImageResampling.BILINEAR	, UpperCAmelCase       :							Optional[Union[str, ChannelDimension]] = None	, **UpperCAmelCase       :							Any	, ):
            A_	     =							get_size_dict(UpperCAmelCase  )
            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()}'''  )
            return resize(
                UpperCAmelCase	, size=(size["height"], size["width"])	, resample=UpperCAmelCase	, data_format=UpperCAmelCase	, **UpperCAmelCase  )





      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							np.ndarray	, UpperCAmelCase       :							float	, UpperCAmelCase       :							Optional[Union[str, ChannelDimension]] = None	, **UpperCAmelCase       :							Union[str, Any]  ):
            return rescale(UpperCAmelCase	, scale=UpperCAmelCase	, data_format=UpperCAmelCase	, **UpperCAmelCase  )





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







      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							ImageInput	, UpperCAmelCase       :							Optional[bool] = None	, UpperCAmelCase       :							Dict[str, int] = None	, UpperCAmelCase       :							PILImageResampling = None	, UpperCAmelCase       :							Optional[bool] = None	, UpperCAmelCase       :							Optional[float] = None	, UpperCAmelCase       :							Optional[bool] = None	, UpperCAmelCase       :							Optional[Union[float, List[float]]] = None	, UpperCAmelCase       :							Optional[Union[float, List[float]]] = None	, UpperCAmelCase       :							Optional[Union[str, TensorType]] = None	, UpperCAmelCase       :							Union[str, ChannelDimension] = ChannelDimension.FIRST	, **UpperCAmelCase       :							List[str]	, ):
            A_	     =							do_resize if do_resize is not None else self.do_resize
            A_	     =							do_rescale if do_rescale is not None else self.do_rescale
            A_	     =							do_normalize if do_normalize is not None else self.do_normalize
            A_	     =							resample if resample is not None else self.resample
            A_	     =							rescale_factor if rescale_factor is not None else self.rescale_factor
            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_	     =							size if size is not None else self.size
            A_	     =							get_size_dict(UpperCAmelCase  )

            A_	     =							make_list_of_images(UpperCAmelCase  )

            if not valid_images(UpperCAmelCase  ):
                  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."  )

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

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

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

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

            A_	     =							[to_channel_dimension_format(UpperCAmelCase	, UpperCAmelCase  ) for image in images]

            A_	     =							{"pixel_values": images}
            return BatchFeature(data=UpperCAmelCase	, tensor_type=UpperCAmelCase  )









__a						:Dict            = {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'

def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							hex_string.strip().lower()
      if not hex_string:
            raise ValueError("Empty string was passed to the function"						)
      A_	     =							hex_string[0] == "-"
      if is_negative:
            A_	     =							hex_string[1:]
      if not all(char in hex_table for char in hex_string						):
            raise ValueError("Non-hexadecimal value was passed to the function"						)
      A_	     =							0
      for char in hex_string:
            A_	     =							16 * decimal_number + hex_table[char]
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()