



"""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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


A_						:    List[str]				=      logging.get_logger(__name__)
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= ['''pixel_values''']





	def __init__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int] = None     ,       _SCREAMING_SNAKE_CASE				:       float = None     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = PILImageResampling.BILINEAR     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Union[int, float] = 1 / 255     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, List[float]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, List[float]]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       )						->	None:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     size if size is not None else {'shortest_edge': 384}
					SCREAMING_SNAKE_CASE						:							List[Any]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     do_resize
					SCREAMING_SNAKE_CASE						:							List[str]							=     size
					# Default value set here for backwards compatibility where the value in config is None
					SCREAMING_SNAKE_CASE						:							int							=     crop_pct if crop_pct is not None else 224 / 256
					SCREAMING_SNAKE_CASE						:							List[str]							=     resample
					SCREAMING_SNAKE_CASE						:							Dict							=     do_rescale
					SCREAMING_SNAKE_CASE						:							Dict							=     rescale_factor
					SCREAMING_SNAKE_CASE						:							int							=     do_normalize
					SCREAMING_SNAKE_CASE						:							int							=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					SCREAMING_SNAKE_CASE						:							Any							=     image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int]     ,       _SCREAMING_SNAKE_CASE				:       float     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = PILImageResampling.BICUBIC     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]     ,       )						->	np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					if "shortest_edge" not in size:
									raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""    )
					SCREAMING_SNAKE_CASE						:							Dict							=     size['shortest_edge']

					if shortest_edge < 384:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									SCREAMING_SNAKE_CASE						:							List[str]							=     int(shortest_edge / crop_pct    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     get_resize_output_image_size(_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[Any]							=     resize(image=_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       resample=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=_SCREAMING_SNAKE_CASE     ,       size=(shortest_edge, shortest_edge)     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    _SCREAMING_SNAKE_CASE     ,       size=(shortest_edge, shortest_edge)     ,       resample=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Union[int, float]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       int     ,       )						->	Any:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Union[float, List[float]]     ,       _SCREAMING_SNAKE_CASE				:       Union[float, List[float]]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Dict     ,       )						->	np.ndarray:





					"""simple docstring"""


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







	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       ImageInput     ,       _SCREAMING_SNAKE_CASE				:       bool = None     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int] = None     ,       _SCREAMING_SNAKE_CASE				:       float = None     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = None     ,       _SCREAMING_SNAKE_CASE				:       bool = None     ,       _SCREAMING_SNAKE_CASE				:       float = None     ,       _SCREAMING_SNAKE_CASE				:       bool = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, List[float]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, List[float]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, TensorType]] = None     ,       _SCREAMING_SNAKE_CASE				:       ChannelDimension = ChannelDimension.FIRST     ,       **_SCREAMING_SNAKE_CASE				:       Dict     ,       )						->	PIL.Image.Image:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE						:							Tuple							=     crop_pct if crop_pct is not None else self.crop_pct
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     resample if resample is not None else self.resample
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE						:							List[Any]							=     rescale_factor if rescale_factor is not None else self.rescale_factor
					SCREAMING_SNAKE_CASE						:							str							=     do_normalize if do_normalize is not None else self.do_normalize
					SCREAMING_SNAKE_CASE						:							str							=     image_mean if image_mean is not None else self.image_mean
					SCREAMING_SNAKE_CASE						:							List[Any]							=     image_std if image_std is not None else self.image_std

					SCREAMING_SNAKE_CASE						:							Any							=     size if size is not None else self.size
					SCREAMING_SNAKE_CASE						:							Any							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     make_list_of_images(_SCREAMING_SNAKE_CASE    )

					if not valid_images(_SCREAMING_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_resize and size["shortest_edge"] < 384 and crop_pct is None:
									raise ValueError('crop_pct must be specified if size < 384.'    )

					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[int]							=     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE						:							Tuple							=     [self.resize(image=_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       crop_pct=_SCREAMING_SNAKE_CASE     ,       resample=_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE						:							int							=     [self.rescale(image=_SCREAMING_SNAKE_CASE     ,       scale=_SCREAMING_SNAKE_CASE    ) for image in images]

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

					SCREAMING_SNAKE_CASE						:							Tuple							=     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							Dict							=     {'pixel_values': images}
					return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,       tensor_type=_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





