





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


if is_vision_available():
		import PIL







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["pixel_values"]


					def __init__(					self	:  List[Any]       ,     __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_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	:  List[Any]       ,     ):





											'''simple docstring'''





											super().__init__(**lowercase_       )
											_snake_case:   int  					=		size if size is not None else {'shortest_edge': 2_24}
											_snake_case:   Any  					=		get_size_dict(lowercase_       ,     default_to_square=lowercase_       )
											_snake_case:   List[str]  					=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
											_snake_case:   List[str]  					=		get_size_dict(lowercase_       ,     default_to_square=lowercase_       ,     param_name='crop_size'       )

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





											'''simple docstring'''





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


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





											'''simple docstring'''





											_snake_case:   Dict  					=		get_size_dict(lowercase_       )
											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(lowercase_       ,     size=(size['height'], size['width'])       ,     data_format=lowercase_       ,     **lowercase_       )


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





											'''simple docstring'''





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


					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	:  Tuple       ,     ):





											'''simple docstring'''





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




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[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	:  Any       ,     ):





											'''simple docstring'''





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

											_snake_case:   int  					=		make_list_of_images(lowercase_       )

											if not valid_images(lowercase_       ):
																	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:
																	_snake_case:   Tuple  					=		[convert_to_rgb(lowercase_       ) for image in images]

											# All transformations expect numpy arrays.
											_snake_case:   List[Any]  					=		[to_numpy_array(lowercase_       ) for image in images]

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

											if do_center_crop:
																	_snake_case:   Union[str, Any]  					=		[self.center_crop(image=lowercase_       ,     size=lowercase_       ) for image in images]

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

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

											_snake_case:   int  					=		[to_channel_dimension_format(lowercase_       ,     lowercase_       ) for image in images]

											_snake_case:   Any  					=		{'pixel_values': images}
											return BatchFeature(data=lowercase_       ,     tensor_type=lowercase_       )












'''simple docstring'''






def       lowercase_      (		lowercase__ = 1				,  lowercase__ = 1000						)   ->int:
						_snake_case:   int  					=		1
						_snake_case:   Union[str, Any]  					=		0

						for divide_by_number in range(lowercase__				,  digit + 1						):
												_snake_case:   list[int]  					=		[]
												_snake_case:   int  					=		numerator
												for _ in range(1				,  digit + 1						):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(lowercase__						):
																														_snake_case:   Union[str, Any]  					=		len(lowercase__						)
																														_snake_case:   Tuple  					=		divide_by_number
																		else:
																								has_been_divided.append(lowercase__						)
																								_snake_case:   Union[str, Any]  					=		now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()





