





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, 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, is_vision_available, logging


if is_vision_available():
 import PIL


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


class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     ['''pixel_values''']



     def __init__(							self       :Optional[int]			,      snake_case       :bool = True			,      snake_case       :Dict[str, int] = None			,      snake_case       :PILImageResampling = PIL.Image.NEAREST			,      snake_case       :bool = False			,      snake_case       :Dict[str, int] = None			,      snake_case       :Union[int, float] = 1 / 255			,      snake_case       :bool = False			,      snake_case       :bool = True			,      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]			,      ):





        '''simple docstring'''




        super().__init__(**snake_case  )
        A_					:				Dict          =			size if size is not None else {"height": 346, "width": 346}
        A_					:				Dict          =			get_size_dict(snake_case  )
        A_					:				List[str]          =			crop_size if crop_size is not None else {"height": 289, "width": 289}
        A_					:				List[Any]          =			get_size_dict(snake_case			,      param_name="crop_size"  )

        A_					:				List[str]          =			do_resize
        A_					:				str          =			size
        A_					:				str          =			resample
        A_					:				Dict          =			do_center_crop
        A_					:				List[Any]          =			crop_size
        A_					:				Optional[Any]          =			do_rescale
        A_					:				Union[str, Any]          =			rescale_factor
        A_					:				Optional[int]          =			rescale_offset
        A_					:				int          =			do_normalize
        A_					:				Dict          =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A_					:				Optional[Any]          =			image_std if image_std is not None else IMAGENET_STANDARD_STD
        A_					:				int          =			include_top



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :np.ndarray			,      snake_case       :Dict[str, int]			,      snake_case       :PILImageResampling = PIL.Image.NEAREST			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :Tuple			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			get_size_dict(snake_case  )
        if "height" not in size or "width" not in size:
           raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"  )
        return resize(
            snake_case			,      size=(size["height"], size["width"])			,      resample=snake_case			,      data_format=snake_case			,      **snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :np.ndarray			,      snake_case       :Dict[str, int]			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :int			,      ):





        '''simple docstring'''




        A_					:				Optional[int]          =			get_size_dict(snake_case  )
        if "height" not in size or "width" not in size:
           raise ValueError(f"The size dictionary must have keys 'height' and '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       :Tuple			,      snake_case       :np.ndarray			,      snake_case       :Union[int, float]			,      snake_case       :bool = True			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :Union[str, Any]			,      ):





        '''simple docstring'''




        if offset:
           A_					:				str          =			(image - 127.5) * scale
           if data_format is not None:
              A_					:				Union[str, Any]          =			to_channel_dimension_format(snake_case			,      snake_case  )
           A_					:				Tuple          =			rescaled_image.astype(np.floataa  )
        else:
           A_					:				Optional[int]          =			rescale(snake_case			,      scale=snake_case			,      data_format=snake_case			,      **snake_case  )
        return rescaled_image



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      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       :Union[str, Any]			,      ):





        '''simple docstring'''




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





     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :ImageInput			,      snake_case       :bool = None			,      snake_case       :Dict[str, int] = None			,      snake_case       :Union[str, Any]=None			,      snake_case       :bool = None			,      snake_case       :Dict[str, int] = None			,      snake_case       :bool = None			,      snake_case       :float = None			,      snake_case       :bool = 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       :ChannelDimension = ChannelDimension.FIRST			,      **snake_case       :List[str]			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			do_resize if do_resize is not None else self.do_resize
        A_					:				Any          =			resample if resample is not None else self.resample
        A_					:				Dict          =			do_center_crop if do_center_crop is not None else self.do_center_crop
        A_					:				Tuple          =			do_rescale if do_rescale is not None else self.do_rescale
        A_					:				Union[str, Any]          =			rescale_factor if rescale_factor is not None else self.rescale_factor
        A_					:				List[str]          =			rescale_offset if rescale_offset is not None else self.rescale_offset
        A_					:				List[str]          =			do_normalize if do_normalize is not None else self.do_normalize
        A_					:				int          =			image_mean if image_mean is not None else self.image_mean
        A_					:				Dict          =			image_std if image_std is not None else self.image_std
        A_					:				int          =			include_top if include_top is not None else self.include_top

        A_					:				Union[str, Any]          =			size if size is not None else self.size
        A_					:				Union[str, Any]          =			get_size_dict(snake_case  )
        A_					:				Tuple          =			crop_size if crop_size is not None else self.crop_size
        A_					:				int          =			get_size_dict(snake_case			,      param_name="crop_size"  )

        A_					:				int          =			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 or resample is None:
           raise ValueError("Size and resample 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."  )

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

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

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

        if do_rescale:
           A_					:				Optional[int]          =			[self.rescale(image=snake_case			,      scale=snake_case			,      offset=snake_case  ) for image in images]

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

        if include_top:
           A_					:				Optional[int]          =			[self.normalize(image=snake_case			,      mean=[0, 0, 0]			,      std=snake_case  ) for image in images]

        A_					:				Optional[Any]          =			[to_channel_dimension_format(snake_case			,      snake_case  ) for image in images]

        A_					:				Optional[Any]          =			{"pixel_values": images}
        return BatchFeature(data=snake_case			,      tensor_type=snake_case  )






def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int = 1000	) ->						str:
   if (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"	)
   elif (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"	)

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   A_					:				List[str]          =			(
       4 * _subsum(_lowerCAmelCase				,				1				,				_lowerCAmelCase	)
       - 2 * _subsum(_lowerCAmelCase				,				4				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				5				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				6				,				_lowerCAmelCase	)
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16	)	)[2:]

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						float:
   A_					:				int          =			0.0
   for sum_index in range(digit_pos_to_extract + precision	):
      A_					:				Optional[int]          =			8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         A_					:				Optional[Any]          =			pow(
             16				,				digit_pos_to_extract - 1 - sum_index				,				_lowerCAmelCase	)
      else:
         A_					:				Tuple          =			pow(16				,				digit_pos_to_extract - 1 - sum_index	)
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
 import doctest

 doctest.testmod()
