


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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
       import PIL

if is_torch_available():
       import torch


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						(		_a    ):
      a :     Optional[Any]											=		["""pixel_values"""]
      def __init__(		self	:	List[Any]      ,       A_	:	bool = True      ,       A_	:	Dict[str, int] = None      ,       A_	:	PILImageResampling = PILImageResampling.BILINEAR      ,       A_	:	bool = True      ,       A_	:	Union[int, float] = 1 / 2_5_5      ,       A_	:	bool = True      ,       A_	:	Dict[str, int] = None      ,       A_	:	bool = True      ,       **A_	:	Optional[int]      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)
       __lowercase									=	size if size is not None else {"""shortest_edge""": 2_2_4}
       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       __lowercase									=	crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
       __lowercase									=	get_size_dict(A_      ,       param_name="""crop_size"""							)

       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	resample
       __lowercase									=	do_rescale
       __lowercase									=	rescale_factor
       __lowercase									=	do_center_crop
       __lowercase									=	crop_size
       __lowercase									=	do_flip_channel_order
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	np.ndarray      ,       A_	:	Dict[str, int]      ,       A_	:	PILImageResampling = PIL.Image.BILINEAR      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       if "shortest_edge" not in size:
        raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''							)
       __lowercase									=	get_resize_output_image_size(A_      ,       size=size["""shortest_edge"""]      ,       default_to_square=A_							)
       return resize(A_      ,       size=A_      ,       resample=A_      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	np.ndarray      ,       A_	:	Dict[str, int]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Tuple      ,       ):






       '''simple docstring'''


       __lowercase									=	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()}'''							)
       return center_crop(A_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	np.ndarray      ,       A_	:	Union[int, float]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Tuple      ,       ):






       '''simple docstring'''


       return rescale(A_      ,       scale=A_      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	np.ndarray      ,       A_	:	Optional[Union[str, ChannelDimension]] = None							):






       '''simple docstring'''


       return flip_channel_order(A_      ,       data_format=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	ImageInput      ,       A_	:	bool = None      ,       A_	:	Dict[str, int] = None      ,       A_	:	PILImageResampling = None      ,       A_	:	bool = None      ,       A_	:	float = None      ,       A_	:	bool = None      ,       A_	:	Dict[str, int] = None      ,       A_	:	bool = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	ChannelDimension = ChannelDimension.FIRST      ,       **A_	:	Tuple      ,       ):






       '''simple docstring'''


       __lowercase									=	do_resize if do_resize is not None else self.do_resize
       __lowercase									=	resample if resample is not None else self.resample
       __lowercase									=	do_rescale if do_rescale is not None else self.do_rescale
       __lowercase									=	rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowercase									=	do_center_crop if do_center_crop is not None else self.do_center_crop
       __lowercase									=	(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

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

       __lowercase									=	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:
        raise ValueError("""Size 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_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

       # All transformations expect numpy arrays.
       __lowercase									=	[to_numpy_array(A_							) for image in images]

       if do_resize:
        __lowercase									=	[self.resize(image=A_      ,       size=A_      ,       resample=A_							) for image in images]

       if do_center_crop:
        __lowercase									=	[self.center_crop(image=A_      ,       size=A_							) for image in images]

       if do_rescale:
        __lowercase									=	[self.rescale(image=A_      ,       scale=A_							) for image in images]

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
        __lowercase									=	[self.flip_channel_order(image=A_							) for image in images]

       __lowercase									=	[to_channel_dimension_format(A_      ,       A_							) for image in images]

       __lowercase									=	{"""pixel_values""": images}
       return BatchFeature(data=A_      ,       tensor_type=A_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[Any]      ,       A_	:	List[Tuple] = None							):






       '''simple docstring'''


       __lowercase									=	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_							):
         __lowercase									=	target_sizes.numpy()

        __lowercase									=	[]

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

       return semantic_segmentation




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 __lowercase									=	0

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

 if n > len(UpperCamelCase__ ):
  __lowercase									=	(
      """Each integral piece of rod must have a corresponding price. """
      f'''Got n = {n} but length of prices = {len(UpperCamelCase__ )}'''
  )
  raise ValueError(UpperCamelCase__ )


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowercase									=	36

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()
