


from typing import Dict, List, Optional, Tuple, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


__lowerCAmelCase	: str  				=logging.get_logger(__name__)
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Tuple						=		['''pixel_values''']


				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Optional[Dict[str, int]] = None			,			lowerCAmelCase__			:PILImageResampling = PILImageResampling.BILINEAR			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Union[int, float] = 1 / 255			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = None			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = None			,			**lowerCAmelCase__			:Tuple			,			)		->			None:
										super().__init__(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size if size is not None else {'''shortest_edge''': 256}
										__SCREAMING_SNAKE_CASE						:     int					=		get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										__SCREAMING_SNAKE_CASE						:     str					=		get_size_dict(lowerCAmelCase__			,			param_name='''crop_size'''	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     int					=		size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		resample
										__SCREAMING_SNAKE_CASE						:     Any					=		do_center_crop
										__SCREAMING_SNAKE_CASE						:     Dict					=		crop_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		do_rescale
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		rescale_factor
										__SCREAMING_SNAKE_CASE						:     Any					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE						:     int					=		image_std if image_std is not None else IMAGENET_STANDARD_STD


				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:np.ndarray			,			lowerCAmelCase__			:Dict[str, int]			,			lowerCAmelCase__			:PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase__			:int			,			)		->			np.ndarray:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__	)
										if "shortest_edge" not in size:
																raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		get_resize_output_image_size(lowerCAmelCase__			,			size=size['''shortest_edge''']			,			default_to_square=lowerCAmelCase__	)
										return resize(lowerCAmelCase__			,			size=lowerCAmelCase__			,			resample=lowerCAmelCase__			,			data_format=lowerCAmelCase__			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:np.ndarray			,			lowerCAmelCase__			:Dict[str, int]			,			lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase__			:str			,			)		->			np.ndarray:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		get_size_dict(lowerCAmelCase__	)
										if "height" not in size or "width" not in size:
																raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''	)
										return center_crop(lowerCAmelCase__			,			size=(size['''height'''], size['''width'''])			,			data_format=lowerCAmelCase__			,			**lowerCAmelCase__	)


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


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


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:ImageInput			,			lowerCAmelCase__			:Optional[bool] = None			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:PILImageResampling = None			,			lowerCAmelCase__			:bool = None			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:Optional[bool] = None			,			lowerCAmelCase__			:Optional[float] = None			,			lowerCAmelCase__			:Optional[bool] = None			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = None			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = None			,			lowerCAmelCase__			:Optional[Union[str, TensorType]] = None			,			lowerCAmelCase__			:Union[str, ChannelDimension] = ChannelDimension.FIRST			,			**lowerCAmelCase__			:Optional[int]			,			)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE						:     Any					=		size if size is not None else self.size
										__SCREAMING_SNAKE_CASE						:     Any					=		get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		do_center_crop if do_center_crop is not None else self.do_center_crop
										__SCREAMING_SNAKE_CASE						:     str					=		crop_size if crop_size is not None else self.crop_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		get_size_dict(lowerCAmelCase__			,			param_name='''crop_size'''	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE						:     int					=		do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		make_list_of_images(lowerCAmelCase__	)

										if not valid_images(lowerCAmelCase__	):
																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.'''	)

										# All transformations expect numpy arrays.
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[to_numpy_array(lowerCAmelCase__	) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE						:     Dict					=		[self.resize(image=lowerCAmelCase__			,			size=lowerCAmelCase__			,			resample=lowerCAmelCase__	) for image in images]

										if do_center_crop:
																__SCREAMING_SNAKE_CASE						:     Tuple					=		[self.center_crop(image=lowerCAmelCase__			,			size=lowerCAmelCase__	) for image in images]

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

										if do_normalize:
																__SCREAMING_SNAKE_CASE						:     int					=		[self.normalize(image=lowerCAmelCase__			,			mean=lowerCAmelCase__			,			std=lowerCAmelCase__	) for image in images]

										__SCREAMING_SNAKE_CASE						:     int					=		[to_channel_dimension_format(lowerCAmelCase__			,			lowerCAmelCase__	) for image in images]

										__SCREAMING_SNAKE_CASE						:     Any					=		{'''pixel_values''': images}
										return BatchFeature(data=lowerCAmelCase__			,			tensor_type=lowerCAmelCase__	)



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:List[Tuple] = None	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.logits

										# Resize logits and compute semantic segmentation maps
										if target_sizes is not None:
																if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''	)

																if is_torch_tensor(lowerCAmelCase__	):
																						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		target_sizes.numpy()

																__SCREAMING_SNAKE_CASE						:     Any					=		[]

																for idx in range(len(lowerCAmelCase__	)	):
																						__SCREAMING_SNAKE_CASE						:     Any					=		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0	)			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=lowerCAmelCase__	)
																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		resized_logits[0].argmax(dim=0	)
																						semantic_segmentation.append(lowerCAmelCase__	)
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		logits.argmax(dim=1	)
																__SCREAMING_SNAKE_CASE						:     Dict					=		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

										return semantic_segmentation




from __future__ import annotations

import bisect


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Any					=		lo + (hi - lo) // 2
												if sorted_collection[mid] < item:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		lo + (hi - lo) // 2
												if sorted_collection[mid] <= item:
																		__SCREAMING_SNAKE_CASE						:     Any					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_left(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_right(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      ) - 1

						while left <= right:
												__SCREAMING_SNAKE_CASE						:     str					=		left + (right - left) // 2
												__SCREAMING_SNAKE_CASE						:     List[str]					=		sorted_collection[midpoint]
												if current_item == item:
																		return midpoint
												elif item < current_item:
																		__SCREAMING_SNAKE_CASE						:     int					=		midpoint - 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		midpoint + 1
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		bisect.bisect_left(lowercase__    ,       lowercase__      )
						if index != len(lowercase__      ) and sorted_collection[index] == item:
												return index
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if right < left:
												return None

						__SCREAMING_SNAKE_CASE						:     int					=		left + (right - left) // 2

						if sorted_collection[midpoint] == item:
												return midpoint
						elif sorted_collection[midpoint] > item:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       lowercase__    ,       midpoint - 1      )
						else:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       midpoint + 1    ,       lowercase__      )


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=input('Enter numbers separated by comma:\n').strip()
						__lowerCAmelCase	: str  				=sorted(int(item) for item in user_input.split(','))
						__lowerCAmelCase	: Tuple  				=int(input('Enter a single number to be found in the list:\n'))
						__lowerCAmelCase	: Tuple  				=binary_search(collection, target)
						if result is None:
												print(f"""{target} was not found in {collection}.""")
						else:
												print(f"""{target} was found at position {result} in {collection}.""")
