



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


if is_vision_available():
				import PIL


lowercase_	       =			logging.get_logger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any	)  ->						List[List[ImageInput]]:
					if isinstance(__lowerCamelCase				,   (list, tuple)	) and isinstance(videos[0]				,   (list, tuple)	) and is_valid_image(videos[0][0]	):
										return videos

					elif isinstance(__lowerCamelCase				,   (list, tuple)	) and is_valid_image(videos[0]	):
										return [videos]

					elif is_valid_image(__lowerCamelCase	):
										return [[videos]]

					raise ValueError(f"""Could not make batched video from {videos}"""	)






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       ["pixel_values"]



	def __init__(						self							:    Optional[Any]				,	snake_case_							:    bool = True				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    PILImageResampling = PILImageResampling.BILINEAR				,	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_							:    Tuple				,	)->							None:
						super().__init__(**snake_case_)
						__lowerCAmelCase	   =size if size is not None else {"""shortest_edge""": 2_24}
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						__lowerCAmelCase	   =crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	param_name="""crop_size""")

						__lowerCAmelCase	   =do_resize
						__lowerCAmelCase	   =size
						__lowerCAmelCase	   =do_center_crop
						__lowerCAmelCase	   =crop_size
						__lowerCAmelCase	   =resample
						__lowerCAmelCase	   =do_rescale
						__lowerCAmelCase	   =rescale_factor
						__lowerCAmelCase	   =do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase	   =image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    np.ndarray				,	snake_case_							:    Dict[str, int]				,	snake_case_							:    PILImageResampling = PILImageResampling.BILINEAR				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    str				,	)->							np.ndarray:
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						if "shortest_edge" in size:
											__lowerCAmelCase	   =get_resize_output_image_size(snake_case_				,	size["""shortest_edge"""]				,	default_to_square=snake_case_)
						elif "height" in size and "width" in size:
											__lowerCAmelCase	   =(size["""height"""], size["""width"""])
						else:
											raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
						return resize(snake_case_				,	size=snake_case_				,	resample=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    np.ndarray				,	snake_case_							:    Dict[str, int]				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Union[str, Any]				,	)->							np.ndarray:
						__lowerCAmelCase	   =get_size_dict(snake_case_)
						if "height" not in size or "width" not in size:
											raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
						return center_crop(snake_case_				,	size=(size["""height"""], size["""width"""])				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    np.ndarray				,	snake_case_							:    Union[int, float]				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Tuple				,	)->							Any:
						return rescale(snake_case_				,	scale=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						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				,	)->							np.ndarray:
						return normalize(snake_case_				,	mean=snake_case_				,	std=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    ImageInput				,	snake_case_							:    bool = None				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    PILImageResampling = 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_							:    Optional[Union[float, List[float]]] = None				,	snake_case_							:    Optional[Union[float, List[float]]] = None				,	snake_case_							:    Optional[ChannelDimension] = ChannelDimension.FIRST				,	)->							np.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.
						__lowerCAmelCase	   =to_numpy_array(snake_case_)

						if do_resize:
											__lowerCAmelCase	   =self.resize(image=snake_case_				,	size=snake_case_				,	resample=snake_case_)

						if do_center_crop:
											__lowerCAmelCase	   =self.center_crop(snake_case_				,	size=snake_case_)

						if do_rescale:
											__lowerCAmelCase	   =self.rescale(image=snake_case_				,	scale=snake_case_)

						if do_normalize:
											__lowerCAmelCase	   =self.normalize(image=snake_case_				,	mean=snake_case_				,	std=snake_case_)

						__lowerCAmelCase	   =to_channel_dimension_format(snake_case_				,	snake_case_)
						return image



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    ImageInput				,	snake_case_							:    bool = None				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    PILImageResampling = 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_							:    Optional[Union[float, List[float]]] = None				,	snake_case_							:    Optional[Union[float, List[float]]] = None				,	snake_case_							:    Optional[Union[str, TensorType]] = None				,	snake_case_							:    ChannelDimension = ChannelDimension.FIRST				,	**snake_case_							:    str				,	)->							PIL.Image.Image:
						__lowerCAmelCase	   =do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase	   =resample if resample is not None else self.resample
						__lowerCAmelCase	   =do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase	   =do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase	   =rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase	   =do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase	   =image_std if image_std is not None else self.image_std

						__lowerCAmelCase	   =size if size is not None else self.size
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	default_to_square=snake_case_)
						__lowerCAmelCase	   =crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase	   =get_size_dict(snake_case_				,	param_name="""crop_size""")

						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.""")

						__lowerCAmelCase	   =make_batched(snake_case_)

						__lowerCAmelCase	   =[
						    [
						        self._preprocess_image(
						            image=snake_case_				,	do_resize=snake_case_				,	size=snake_case_				,	resample=snake_case_				,	do_center_crop=snake_case_				,	crop_size=snake_case_				,	do_rescale=snake_case_				,	rescale_factor=snake_case_				,	do_normalize=snake_case_				,	image_mean=snake_case_				,	image_std=snake_case_				,	data_format=snake_case_				,	)
						        for img in video
						    ]
						    for video in videos
						]

						__lowerCAmelCase	   ={"""pixel_values""": videos}
						return BatchFeature(data=snake_case_				,	tensor_type=snake_case_)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




