

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__  :      List[str]			=    logging.get_logger(__name__)



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  ['pixel_values']

  def __init__(					self						,		_A = True						,		_A = None						,		_A = PILImageResampling.BILINEAR						,		_A = True						,		_A = 1 / 255						,		_A = True						,		_A = None						,		_A = True						,		**_A						,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'shortest_edge': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 256, 'width': 256}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='crop_size')

      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						resample
      SCREAMING_SNAKE_CASE_									=						do_rescale
      SCREAMING_SNAKE_CASE_									=						rescale_factor
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_flip_channel_order

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = PIL.Image.BILINEAR						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						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()}""")
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      return rescale(_A						,		scale=_A						,		data_format=_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      return flip_channel_order(_A						,		data_format=_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = ChannelDimension.FIRST						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_									=						resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_									=						do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_									=						rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_									=						do_center_crop if do_center_crop is not None else self.do_center_crop
      SCREAMING_SNAKE_CASE_									=						(
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      SCREAMING_SNAKE_CASE_									=						size if size is not None else self.size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else self.crop_size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='crop_size')

      SCREAMING_SNAKE_CASE_									=						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.
      SCREAMING_SNAKE_CASE_									=						[to_numpy_array(_A) for image in images]

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

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

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

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

      SCREAMING_SNAKE_CASE_									=						[to_channel_dimension_format(_A						,		_A) for image in images]

      SCREAMING_SNAKE_CASE_									=						{'pixel_values': images}
      return BatchFeature(data=_A						,		tensor_type=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						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):
              SCREAMING_SNAKE_CASE_									=						target_sizes.numpy()

          SCREAMING_SNAKE_CASE_									=						[]

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

      return semantic_segmentation





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int = 200       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[1, 2, 5, 10, 20, 50, 100, 200]
    SCREAMING_SNAKE_CASE_									=						[0] * (pence + 1)
    SCREAMING_SNAKE_CASE_									=						1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_SCREAMING_SNAKE_CASE ,   pence + 1 ,   1       ):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682


