





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


A__:    int  	=	logging.get_logger(__name__)




class        _a    (    SCREAMING_SNAKE_CASE_):

      """simple docstring"""


      UpperCamelCase__  		=       ["""pixel_values"""]






      def __init__(		self:			List[str]	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			PILImageResampling = PILImageResampling.BILINEAR	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Union[int, float] = 1 / 255	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			bool = True	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


          super().__init__(**_a					)
          UpperCamelCase__:	Union[str, Any]					=     size if size is not None else {'shortest_edge': 224}
          UpperCamelCase__:	int					=     get_size_dict(_a	,    default_to_square=_a					)
          UpperCamelCase__:	Optional[int]					=     crop_size if crop_size is not None else {'height': 256, 'width': 256}
          UpperCamelCase__:	List[str]					=     get_size_dict(_a	,    param_name="crop_size"					)

          UpperCamelCase__:	Dict					=     do_resize
          UpperCamelCase__:	Optional[int]					=     size
          UpperCamelCase__:	List[Any]					=     resample
          UpperCamelCase__:	Optional[int]					=     do_rescale
          UpperCamelCase__:	Dict					=     rescale_factor
          UpperCamelCase__:	Optional[Any]					=     do_center_crop
          UpperCamelCase__:	Optional[int]					=     crop_size
          UpperCamelCase__:	Optional[Any]					=     do_flip_channel_order






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Dict[str, int]	,    __lowerCamelCase:			PILImageResampling = PIL.Image.BILINEAR	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			Union[str, Any]	,    ):
          '''simple docstring'''


          UpperCamelCase__:	int					=     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()}"					)
          UpperCamelCase__:	int					=     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 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Dict[str, int]	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


          UpperCamelCase__:	int					=     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 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Union[int, float]	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			Any	,    ):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None					):
          '''simple docstring'''


          return flip_channel_order(_a	,    data_format=_a					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			ImageInput	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			PILImageResampling = None	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			float = None	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			Optional[Union[str, TensorType]] = None	,    __lowerCamelCase:			ChannelDimension = ChannelDimension.FIRST	,    **__lowerCamelCase:			str	,    ):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__:	Tuple					=     resample if resample is not None else self.resample
          UpperCamelCase__:	List[Any]					=     do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__:	Optional[Any]					=     rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__:	Tuple					=     do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase__:	Optional[Any]					=     (
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          UpperCamelCase__:	Dict					=     size if size is not None else self.size
          UpperCamelCase__:	Optional[Any]					=     get_size_dict(_a	,    default_to_square=_a					)
          UpperCamelCase__:	Any					=     crop_size if crop_size is not None else self.crop_size
          UpperCamelCase__:	Dict					=     get_size_dict(_a	,    param_name="crop_size"					)

          UpperCamelCase__:	List[str]					=     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.
          UpperCamelCase__:	str					=     [to_numpy_array(_a					) for image in images]

          if do_resize:
              UpperCamelCase__:	str					=     [self.resize(image=_a	,    size=_a	,    resample=_a					) for image in images]

          if do_center_crop:
              UpperCamelCase__:	Optional[int]					=     [self.center_crop(image=_a	,    size=_a					) for image in images]

          if do_rescale:
              UpperCamelCase__:	List[Any]					=     [self.rescale(image=_a	,    scale=_a					) for image in images]

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

          UpperCamelCase__:	List[str]					=     [to_channel_dimension_format(_a	,    _a					) for image in images]

          UpperCamelCase__:	List[str]					=     {'pixel_values': images}
          return BatchFeature(data=_a	,    tensor_type=_a					)






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			List[Tuple] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     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__:	Dict					=     target_sizes.numpy()

              UpperCamelCase__:	Tuple					=     []

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

          return semantic_segmentation






'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






