


'''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, 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


UpperCAmelCase_      :		Optional[Any]          =		logging.get_logger(__name__)







class 					a			(				__a  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		['pixel_values']




 def __init__(    self		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = PIL.Image.BICUBIC		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = 1 / 2_5_5		,      lowerCamelCase_ = True		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    Dict:
    super().__init__(**_lowerCamelCase      )
    _a					:  List[str]						=  size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
    _a					:  Any						=  get_size_dict(_lowerCamelCase      )
    _a					:  List[Any]						=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
    _a					:  Any						=  get_size_dict(_lowerCamelCase		,      param_name='crop_size'      )

    _a					:  Optional[int]						=  do_resize
    _a					:  Optional[int]						=  size
    _a					:  Tuple						=  resample
    _a					:  Optional[Any]						=  do_center_crop
    _a					:  Tuple						=  crop_size
    _a					:  Optional[int]						=  do_rescale
    _a					:  Union[str, Any]						=  rescale_factor
    _a					:  Tuple						=  do_normalize
    _a					:  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _a					:  Optional[int]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = PIL.Image.BICUBIC		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    int:
    _a					:  str						=  get_size_dict(_lowerCamelCase      )
    if "height" not in size or "width" not in size:
       raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
    return resize(
        _lowerCamelCase		,      size=(size['height'], size['width'])		,      resample=_lowerCamelCase		,      data_format=_lowerCamelCase		,      **_lowerCamelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    Union[str, Any]:
    _a					:  List[Any]						=  get_size_dict(_lowerCamelCase      )
    if "height" not in size or "width" not in size:
       raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
    return center_crop(_lowerCamelCase		,      size=(size['height'], size['width'])		,      data_format=_lowerCamelCase		,      **_lowerCamelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    List[Any]:
    return rescale(_lowerCamelCase		,      scale=_lowerCamelCase		,      data_format=_lowerCamelCase		,      **_lowerCamelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    str:
    return normalize(_lowerCamelCase		,      mean=_lowerCamelCase		,      std=_lowerCamelCase		,      data_format=_lowerCamelCase		,      **_lowerCamelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_=None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = ChannelDimension.FIRST		,      **lowerCamelCase_		,      )      ->    Tuple:
    _a					:  Any						=  do_resize if do_resize is not None else self.do_resize
    _a					:  List[str]						=  resample if resample is not None else self.resample
    _a					:  List[Any]						=  do_center_crop if do_center_crop is not None else self.do_center_crop
    _a					:  List[str]						=  do_rescale if do_rescale is not None else self.do_rescale
    _a					:  Dict						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    _a					:  Optional[Any]						=  do_normalize if do_normalize is not None else self.do_normalize
    _a					:  Optional[Any]						=  image_mean if image_mean is not None else self.image_mean
    _a					:  Dict						=  image_std if image_std is not None else self.image_std

    _a					:  Tuple						=  size if size is not None else self.size
    _a					:  Dict						=  get_size_dict(_lowerCamelCase      )
    _a					:  Any						=  crop_size if crop_size is not None else self.crop_size
    _a					:  Dict						=  get_size_dict(_lowerCamelCase		,      param_name='crop_size'      )

    _a					:  int						=  make_list_of_images(_lowerCamelCase      )

    if not valid_images(_lowerCamelCase      ):
       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_center_crop and crop_size is None:
       raise ValueError('Crop size must be specified if do_center_crop 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.
    _a					:  Any						=  [to_numpy_array(_lowerCamelCase      ) for image in images]

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

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

    if do_rescale:
       _a					:  Optional[int]						=  [self.rescale(image=_lowerCamelCase		,      scale=_lowerCamelCase      ) for image in images]

    if do_normalize:
       _a					:  Dict						=  [self.normalize(image=_lowerCamelCase		,      mean=_lowerCamelCase		,      std=_lowerCamelCase      ) for image in images]

    _a					:  Optional[int]						=  [to_channel_dimension_format(_lowerCamelCase		,      _lowerCamelCase      ) for image in images]

    _a					:  Any						=  {'pixel_values': images}
    return BatchFeature(data=_lowerCamelCase		,      tensor_type=_lowerCamelCase      )



'''simple docstring'''

def snake_case_      (					__snake_case  :							int = 200)			->					int:
   lowerCAmelCase_			=      [1, 2, 5, 10, 20, 50, 100, 200]
   lowerCAmelCase_			=      [0] * (pence + 1)
   lowerCAmelCase_			=      1  # base case: 1 way to make 0 pence

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


if __name__ == "__main__":
 assert solution(2_00) == 7_36_82





