



'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
     import PIL

class 							a__( lowercase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  Dict = True						,      __snake_case			:  Optional[Any] = None						,      __snake_case			:  Tuple = PILImageResampling.BICUBIC						,      __snake_case			:  Union[str, Any] = True						,      __snake_case			:  Dict = None						,      __snake_case			:  List[Any] = True						,      __snake_case			:  int = 1 / 2_55						,      __snake_case			:  List[str] = True						,      __snake_case			:  List[Any] = None						,      __snake_case			:  List[str] = None						,      __snake_case			:  Union[str, Any] = True						,      **__snake_case			:  Tuple						,      ):
      super().__init__(**lowercase_     )
      a						: List[str]					       =					size if size is not None else {"shortest_edge": 2_24}
      a						: Tuple					       =					get_size_dict(lowercase_						,      default_to_square=lowercase_     )
      a						: Union[str, Any]					       =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
      a						: int					       =					get_size_dict(lowercase_						,      default_to_square=lowercase_						,      param_name='crop_size'     )

      a						: Any					       =					do_resize
      a						: List[Any]					       =					size
      a						: Union[str, Any]					       =					resample
      a						: int					       =					do_center_crop
      a						: str					       =					crop_size
      a						: Optional[Any]					       =					do_rescale
      a						: Tuple					       =					rescale_factor
      a						: Union[str, Any]					       =					do_normalize
      a						: Dict					       =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a						: Tuple					       =					image_std if image_std is not None else OPENAI_CLIP_STD
      a						: List[Any]					       =					do_convert_rgb







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[Any]						,      __snake_case			:  str = PILImageResampling.BICUBIC						,      __snake_case			:  Dict = None						,      **__snake_case			:  Union[str, Any]						,      ):
      a						: List[Any]					       =					get_size_dict(lowercase_						,      default_to_square=lowercase_     )
      if "shortest_edge" not in size:
        raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
      a						: str					       =					get_resize_output_image_size(lowercase_						,      size=size['shortest_edge']						,      default_to_square=lowercase_     )
      return resize(lowercase_						,      size=lowercase_						,      resample=lowercase_						,      data_format=lowercase_						,      **lowercase_     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[Any] = None						,      **__snake_case			:  Optional[int]						,      ):
      a						: Union[str, Any]					       =					get_size_dict(lowercase_     )
      if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""     )
      return center_crop(lowercase_						,      size=(size['height'], size['width'])						,      data_format=lowercase_						,      **lowercase_     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  List[str]						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[int] = None						,      **__snake_case			:  List[Any]						,      ):
      return rescale(lowercase_						,      scale=lowercase_						,      data_format=lowercase_						,      **lowercase_     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  List[Any]						,      __snake_case			:  str = None						,      **__snake_case			:  Tuple						,      ):
      return normalize(lowercase_						,      mean=lowercase_						,      std=lowercase_						,      data_format=lowercase_						,      **lowercase_     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[str] = None						,      __snake_case			:  Any = None						,      __snake_case			:  Tuple = None						,      __snake_case			:  int = None						,      __snake_case			:  Union[str, Any] = None						,      __snake_case			:  Tuple = None						,      __snake_case			:  List[Any] = None						,      __snake_case			:  List[Any] = None						,      __snake_case			:  Optional[Any] = None						,      __snake_case			:  int = None						,      __snake_case			:  Any = None						,      __snake_case			:  str = None						,      __snake_case			:  List[str] = ChannelDimension.FIRST						,      **__snake_case			:  Dict						,      ):
      a						: List[Any]					       =					do_resize if do_resize is not None else self.do_resize
      a						: List[str]					       =					size if size is not None else self.size
      a						: str					       =					get_size_dict(lowercase_						,      param_name='size'						,      default_to_square=lowercase_     )
      a						: Optional[Any]					       =					resample if resample is not None else self.resample
      a						: Union[str, Any]					       =					do_center_crop if do_center_crop is not None else self.do_center_crop
      a						: Tuple					       =					crop_size if crop_size is not None else self.crop_size
      a						: List[Any]					       =					get_size_dict(lowercase_						,      param_name='crop_size'						,      default_to_square=lowercase_     )
      a						: Union[str, Any]					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: List[str]					       =					rescale_factor if rescale_factor is not None else self.rescale_factor
      a						: Any					       =					do_normalize if do_normalize is not None else self.do_normalize
      a						: List[Any]					       =					image_mean if image_mean is not None else self.image_mean
      a						: Optional[Any]					       =					image_std if image_std is not None else self.image_std
      a						: str					       =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a						: Tuple					       =					make_list_of_images(lowercase_     )

      if not valid_images(lowercase_     ):
        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.'     )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
        a						: List[str]					       =					[convert_to_rgb(lowercase_     ) for image in images]

      # All transformations expect numpy arrays.
      a						: Dict					       =					[to_numpy_array(lowercase_     ) for image in images]

      if do_resize:
        a						: List[str]					       =					[self.resize(image=lowercase_						,      size=lowercase_						,      resample=lowercase_     ) for image in images]

      if do_center_crop:
        a						: List[str]					       =					[self.center_crop(image=lowercase_						,      size=lowercase_     ) for image in images]

      if do_rescale:
        a						: Optional[Any]					       =					[self.rescale(image=lowercase_						,      scale=lowercase_     ) for image in images]

      if do_normalize:
        a						: List[Any]					       =					[self.normalize(image=lowercase_						,      mean=lowercase_						,      std=lowercase_     ) for image in images]

      a						: str					       =					[to_channel_dimension_format(lowercase_						,      lowercase_     ) for image in images]

      a						: Tuple					       =					{"pixel_values": images}
      return BatchFeature(data=lowercase_						,      tensor_type=lowercase_     )




'''simple docstring'''






from math import factorial, pi
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'    )

  a						: Dict					       =					float(_A    )
  a						: List[Any]					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'    )

  a						: int					       =					float(_A    )
  a						: str					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))