



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


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     ["pixel_values"]



					def __init__(  self       :							List[str]					,       _snake_case       :							bool = True					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							PILImageResampling = PILImageResampling.BICUBIC					,       _snake_case       :							bool = True					,       _snake_case       :							Union[int, float] = 1 / 2_55					,       _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       :							int					,       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )
								A__	  =     size if size is not None else {'height': 3_84, 'width': 3_84}
								A__	  =     get_size_dict(_snake_case					,       default_to_square=_snake_case       )

								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      _a				(  self       :							int					,       _snake_case       :							np.ndarray					,       _snake_case       :							Dict[str, int]					,       _snake_case       :							PILImageResampling = PILImageResampling.BICUBIC					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							List[Any]					,       ):






								"""simple docstring"""


								A__	  =     get_size_dict(_snake_case					,       default_to_square=_snake_case       )
								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(_snake_case					,       size=_snake_case					,       resample=_snake_case					,       data_format=_snake_case					,       **_snake_case       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							np.ndarray					,       _snake_case       :							Union[int, float]					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


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



					def      _a				(  self       :							int					,       _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       :							Tuple					,       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Dict					,       _snake_case       :							ImageInput					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Dict[str, int]] = None					,       _snake_case       :							PILImageResampling = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[float] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       _snake_case       :							Optional[Union[float, List[float]]] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							bool = None					,       _snake_case       :							ChannelDimension = ChannelDimension.FIRST					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								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(_snake_case					,       default_to_square=_snake_case       )

								A__	  =     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_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(_snake_case       ) for image in images]

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

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

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

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

								A__	  =     [to_channel_dimension_format(_snake_case					,       _snake_case       ) for image in images]

								A__	  =     BatchFeature(data={'pixel_values': images}					,       tensor_type=_snake_case       )

								return encoded_outputs









def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


if __name__ == "__main__":
							print(f'{solution() = }')




