




"""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,
    get_resize_output_image_size,
    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    :		Any			   =       logging.get_logger(__name__)







class   A(						SCREAMING_SNAKE_CASE__       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''pixel_values''']

 def __init__(    self :  List[str]						,							A_ :  bool = True						,							A_ :  Dict[str, int] = None						,							A_ :  float = None						,							A_ :  PILImageResampling = PILImageResampling.BILINEAR						,							A_ :  bool = True						,							A_ :  Union[int, float] = 1 / 255						,							A_ :  bool = True						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							**A_ :  Dict						,							)       ->     None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_							=   size if size is not None else {'shortest_edge': 384}
       lowerCamelCase_							=   get_size_dict(A_						,							default_to_square=A_  )

       lowerCamelCase_							=   do_resize
       lowerCamelCase_							=   size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase_							=   crop_pct if crop_pct is not None else 224 / 256
       lowerCamelCase_							=   resample
       lowerCamelCase_							=   do_rescale
       lowerCamelCase_							=   rescale_factor
       lowerCamelCase_							=   do_normalize
       lowerCamelCase_							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_							=   image_std if image_std is not None else IMAGENET_STANDARD_STD

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

       """simple docstring"""






       lowerCamelCase_							=   get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""  )
       lowerCamelCase_							=   size['shortest_edge']

       if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             lowerCamelCase_							=   int(shortest_edge / crop_pct  )
             lowerCamelCase_							=   get_resize_output_image_size(A_						,							size=A_						,							default_to_square=A_  )
             lowerCamelCase_							=   resize(image=A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=A_						,							size=(shortest_edge, shortest_edge)						,							data_format=A_						,							**A_  )
       else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 A_						,							size=(shortest_edge, shortest_edge)						,							resample=A_						,							data_format=A_						,							**A_  )

 def     a__			(    self :  str						,							A_ :  np.ndarray						,							A_ :  Union[int, float]						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  int						,							)       ->     Tuple:

       """simple docstring"""






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

 def     a__			(    self :  Optional[Any]						,							A_ :  np.ndarray						,							A_ :  Union[float, List[float]]						,							A_ :  Union[float, List[float]]						,							A_ :  Optional[Union[str, ChannelDimension]] = None						,							**A_ :  Any						,							)       ->     np.ndarray:

       """simple docstring"""






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





 def     a__			(    self :  Tuple						,							A_ :  ImageInput						,							A_ :  bool = None						,							A_ :  Dict[str, int] = None						,							A_ :  float = None						,							A_ :  PILImageResampling = None						,							A_ :  bool = None						,							A_ :  float = None						,							A_ :  bool = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[float, List[float]]] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  ChannelDimension = ChannelDimension.FIRST						,							**A_ :  Dict						,							)       ->     PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_							=   do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_							=   crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase_							=   resample if resample is not None else self.resample
       lowerCamelCase_							=   do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_							=   rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_							=   do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_							=   image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_							=   image_std if image_std is not None else self.image_std

       lowerCamelCase_							=   size if size is not None else self.size
       lowerCamelCase_							=   get_size_dict(A_						,							default_to_square=A_  )

       lowerCamelCase_							=   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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'  )

       if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'  )

       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_							=   [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_							=   [self.resize(image=A_						,							size=A_						,							crop_pct=A_						,							resample=A_  ) for image in images]

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

       if do_normalize:
             lowerCamelCase_							=   [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_							=   [to_channel_dimension_format(A_						,							A_  ) for image in images]

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









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()



