

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase    				=     logging.get_logger(__name__)





class UpperCAmelCase_    ( _UpperCAmelCase ):



 """simple docstring"""







 _lowercase     :					Tuple      =						['''pixel_values''']

 def __init__(		self :	Dict     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,						A_ :	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      super().__init__(**lowerCamelCase_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {'''shortest_edge''': 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(lowerCamelCase_     ,						default_to_square=lowerCamelCase_       )
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      _lowerCAmelCase   :					Dict        =    get_size_dict(lowerCamelCase_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					List[str]        =    do_resize
      _lowerCAmelCase   :					Tuple        =    size
      _lowerCAmelCase   :					Optional[Any]        =    resample
      _lowerCAmelCase   :					Any        =    do_center_crop
      _lowerCAmelCase   :					List[str]        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Any        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[str]        =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _lowerCAmelCase   :					Optional[Any]        =    image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def    __magic_name__       (		self :	List[str]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    get_size_dict(lowerCamelCase_     ,						default_to_square=lowerCamelCase_       )
      # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
      if "shortest_edge" in size:
           _lowerCAmelCase   :					str        =    int((2_5_6 / 2_2_4) * size["shortest_edge"]       )
           _lowerCAmelCase   :					Any        =    get_resize_output_image_size(lowerCamelCase_     ,						size=lowerCamelCase_     ,						default_to_square=lowerCamelCase_       )
           _lowerCAmelCase   :					Optional[int]        =    {'''height''': output_size[0], '''width''': output_size[1]}
      if "height" not in size_dict or "width" not in size_dict:
           raise ValueError(
               F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''       )
      return resize(
          lowerCamelCase_     ,						size=(size_dict["height"], size_dict["width"])     ,						resample=lowerCamelCase_     ,						data_format=lowerCamelCase_     ,						**lowerCamelCase_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    get_size_dict(lowerCamelCase_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(lowerCamelCase_     ,						size=(size["height"], size["width"])     ,						data_format=lowerCamelCase_     ,						**lowerCamelCase_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      return normalize(lowerCamelCase_     ,						mean=lowerCamelCase_     ,						std=lowerCamelCase_     ,						data_format=lowerCamelCase_     ,						**lowerCamelCase_       )


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[float] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Union[float, Iterable[float]]] = None     ,						A_ :	Optional[Union[float, Iterable[float]]] = None     ,						A_ :	Optional[TensorType] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Optional[Any]        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					Dict        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					Optional[int]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Optional[int]        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					Dict        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					Union[str, Any]        =    size if size is not None else self.size
      _lowerCAmelCase   :					Union[str, Any]        =    get_size_dict(lowerCamelCase_     ,						default_to_square=lowerCamelCase_       )
      _lowerCAmelCase   :					str        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(lowerCamelCase_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    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:
           raise ValueError("Size 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.
      _lowerCAmelCase   :					List[Any]        =    [to_numpy_array(lowerCamelCase_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(lowerCamelCase_     ,						lowerCamelCase_     ,						lowerCamelCase_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.center_crop(lowerCamelCase_     ,						lowerCamelCase_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Tuple        =    [self.rescale(lowerCamelCase_     ,						lowerCamelCase_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(lowerCamelCase_     ,						lowerCamelCase_     ,						lowerCamelCase_       ) for image in images]

      _lowerCAmelCase   :					List[str]        =    [to_channel_dimension_format(lowerCamelCase_     ,						lowerCamelCase_       ) for image in images]

      _lowerCAmelCase   :					Any        =    {'''pixel_values''': images}
      return BatchFeature(data=lowerCamelCase_     ,						tensor_type=lowerCamelCase_       )









def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      "list[int]":

     """simple docstring"""







     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"				)

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2		,							upper_limit + 1				):
          for j in range(SCREAMING_SNAKE_CASE				):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
  print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
  print('\n*** Enter -1 at any time to quit ***')
  print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
  try:
    while True:
      __UpperCAmelCase    				=     int(input().strip())
      if N < 0:
        print('\n********* Goodbye!! ************')
        break
      else:
        print(f'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print('Try another upper limit for the sequence: ', end='')
  except (NameError, ValueError):
    print('\n********* Invalid input, goodbye! ************\n')

  import doctest

  doctest.testmod()






