





'''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


lowerCAmelCase :					str 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: str     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = PIL.Image.BICUBIC     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: bool = True     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :str         =			size if size is not None else {"height": 256, "width": 256}
        lowercase_      :Optional[Any]         =			get_size_dict(lowercase						)
        lowercase_      :Dict         =			crop_size if crop_size is not None else {"height": 224, "width": 224}
        lowercase_      :Optional[int]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

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





     def 							lowercase__				(			self		: int     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PIL.Image.BICUBIC     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: str     ,				):





        """simple docstring"""




        lowercase_      :Optional[int]         =			get_size_dict(lowercase						)
        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(
            lowercase     ,				size=(size["height"], size["width"])     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: List[Any]     ,				):





        """simple docstring"""




        lowercase_      :Optional[int]         =			get_size_dict(lowercase						)
        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(lowercase     ,				size=(size["height"], size["width"])     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: np.ndarray     ,				lowercase		: Union[int, float]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str     ,				lowercase		: np.ndarray     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Optional[Any]     ,				):





        """simple docstring"""




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







     def 							lowercase__				(			self		: str     ,				lowercase		: ImageInput     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: Any=None     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = None     ,				lowercase		: float = None     ,				lowercase		: bool = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: ChannelDimension = ChannelDimension.FIRST     ,				**lowercase		: List[Any]     ,				):





        """simple docstring"""




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

        lowercase_      :Optional[Any]         =			size if size is not None else self.size
        lowercase_      :Tuple         =			get_size_dict(lowercase						)
        lowercase_      :str         =			crop_size if crop_size is not None else self.crop_size
        lowercase_      :Union[str, Any]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

        lowercase_      :Optional[int]         =			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 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.
        lowercase_      :Any         =			[to_numpy_array(lowercase						) for image in images]

        if do_resize:
           lowercase_      :int         =			[self.resize(image=lowercase     ,				size=lowercase     ,				resample=lowercase						) for image in images]

        if do_center_crop:
           lowercase_      :Tuple         =			[self.center_crop(image=lowercase     ,				size=lowercase						) for image in images]

        if do_rescale:
           lowercase_      :Union[str, Any]         =			[self.rescale(image=lowercase     ,				scale=lowercase						) for image in images]

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

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

        lowercase_      :Union[str, Any]         =			{"pixel_values": images}
        return BatchFeature(data=lowercase     ,				tensor_type=lowercase						)









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : int | float | str			):
   try:
      lowercase_      :Optional[int]         =			float(__lowerCamelCase			)
   except ValueError:
      raise ValueError("Please enter a valid number"			)
   lowercase_      :Dict         =			decimal - int(__lowerCamelCase			)
   if fractional_part == 0:
      return int(__lowerCamelCase			), 1
   else:
      lowercase_      :Tuple         =			len(str(__lowerCamelCase			).split("."			)[1]			)
      lowercase_      :Optional[Any]         =			int(decimal * (10**number_of_frac_digits)			)
      lowercase_      :Dict         =			10**number_of_frac_digits
      lowercase_      ,							lowercase_      :Optional[int]         =			denominator, numerator
      while True:
         lowercase_      :Any         =			dividend % divisor
         if remainder == 0:
            break
         lowercase_      ,							lowercase_      :Optional[int]         =			divisor, remainder
      lowercase_      ,							lowercase_      :Optional[int]         =			numerator / divisor, denominator / divisor
      return int(__lowerCamelCase			), int(__lowerCamelCase			)


if __name__ == "__main__":
 print(F'''{decimal_to_fraction(2) = }''')
 print(F'''{decimal_to_fraction(89.0) = }''')
 print(F'''{decimal_to_fraction('67') = }''')
 print(F'''{decimal_to_fraction('45.0') = }''')
 print(F'''{decimal_to_fraction(1.5) = }''')
 print(F'''{decimal_to_fraction('6.25') = }''')
 print(F'''{decimal_to_fraction('78td') = }''')


