'''simple docstring'''




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


__SCREAMING_SNAKE_CASE       :     int     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      Union[int, Iterable[int]]			,	_UpperCAmelCase  :      bool			,	_UpperCAmelCase  :      int    )    ->    Tuple[int, int]:


       """simple docstring"""







       def constraint_to_multiple_of(_UpperCAmelCase  :      Tuple			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Any=0			,	_UpperCAmelCase  :      List[Any]=None    ):
              _UpperCAmelCase     :  Tuple							      =    round(val / multiple    ) * multiple

              if max_val is not None and x > max_val:
                     _UpperCAmelCase     :  Any							      =    math.floor(val / multiple    ) * multiple

              if x < min_val:
                     _UpperCAmelCase     :  Optional[Any]							      =    math.ceil(val / multiple    ) * multiple

              return x

       _UpperCAmelCase     :  str							      =    (output_size, output_size) if isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) else output_size

       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    get_image_size(_UpperCAmelCase    )
       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    output_size

       # determine new height and width
       _UpperCAmelCase     :  List[str]							      =    output_height / input_height
       _UpperCAmelCase     :  str							      =    output_width / input_width

       if keep_aspect_ratio:
              # scale as little as possible
              if abs(1 - scale_width    ) < abs(1 - scale_height    ):
                     # fit width
                     _UpperCAmelCase     :  Any							      =    scale_width
              else:
                     # fit height
                     _UpperCAmelCase     :  List[Any]							      =    scale_height

       _UpperCAmelCase     :  Union[str, Any]							      =    constraint_to_multiple_of(scale_height * input_height			,	multiple=_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[int]							      =    constraint_to_multiple_of(scale_width * input_width			,	multiple=_UpperCAmelCase    )

       return (new_height, new_width)


class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						["pixel_values"]




      def __init__(       self						:				List[str]						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				PILImageResampling = PILImageResampling.BILINEAR						,					A						:				bool = False						,					A						:				int = 1						,					A						:				bool = True						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					**A						:				Dict						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  Optional[Any]							      =    size if size is not None else {"height": 384, "width": 384}
             _UpperCAmelCase     :  List[Any]							      =    get_size_dict(A       )
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  Any							      =    keep_aspect_ratio
             _UpperCAmelCase     :  Any							      =    ensure_multiple_of
             _UpperCAmelCase     :  Dict							      =    resample
             _UpperCAmelCase     :  List[str]							      =    do_rescale
             _UpperCAmelCase     :  List[Any]							      =    rescale_factor
             _UpperCAmelCase     :  str							      =    do_normalize
             _UpperCAmelCase     :  Tuple							      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _UpperCAmelCase     :  Optional[Any]							      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def      _A	(       self						:				List[str]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				bool = False						,					A						:				int = 1						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				int						,					):
             _UpperCAmelCase     :  Optional[Any]							      =    get_size_dict(A       )
             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()}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    get_resize_output_image_size(
                 A						,					output_size=(size["height"], size["width"])						,					keep_aspect_ratio=A						,					multiple=A						,					)
             return resize(A						,					size=A						,					resample=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Union[int, float]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Union[str, Any]						,					):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




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




      def      _A	(       self						:				List[Any]						,					A						:				ImageInput						,					A						:				bool = None						,					A						:				int = None						,					A						:				bool = None						,					A						:				int = None						,					A						:				PILImageResampling = None						,					A						:				bool = None						,					A						:				float = None						,					A						:				bool = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					A						:				ChannelDimension = ChannelDimension.FIRST						,					**A						:				Any						,					):
             _UpperCAmelCase     :  Union[str, Any]							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  List[Any]							      =    size if size is not None else self.size
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
             _UpperCAmelCase     :  List[str]							      =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
             _UpperCAmelCase     :  List[Any]							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  Tuple							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  str							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  Any							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  List[Any]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  Any							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  Optional[int]							      =    make_list_of_images(A       )

             if not valid_images(A       ):
                    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."       )

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

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

             if do_rescale:
                    _UpperCAmelCase     :  Union[str, Any]							      =    [self.rescale(image=A						,					scale=A       ) for image in images]

             if do_normalize:
                    _UpperCAmelCase     :  Tuple							      =    [self.normalize(image=A						,					mean=A						,					std=A       ) for image in images]

             _UpperCAmelCase     :  Tuple							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  Any							      =    {"pixel_values": images}
             return BatchFeature(data=A						,					tensor_type=A       )





      def      _A	(       self						:				Optional[int]						,					A						:				List[str]						,					A						:				List[Tuple] = None       ):
             _UpperCAmelCase     :  List[str]							      =    outputs.logits

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

                    if is_torch_tensor(A       ):
                           _UpperCAmelCase     :  Optional[int]							      =    target_sizes.numpy()

                    _UpperCAmelCase     :  Any							      =    []

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

             return semantic_segmentation







def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



