
'''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


a_     =  logging.get_logger(__name__)






class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  ["""pixel_values"""]





 def __init__(      self	,    lowercase_ = True	,    lowercase_ = None	,    lowercase_ = PIL.Image.NEAREST	,    lowercase_ = False	,    lowercase_ = None	,    lowercase_ = 1 / 2_55	,    lowercase_ = False	,    lowercase_ = True	,    lowercase_ = True	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = True	,    **lowercase_	,    ):
     super().__init__(**lowercase_)
     snake_case_	:      int            =  size if size is not None else {"height": 3_46, "width": 3_46}
     snake_case_	:      Union[str, Any]            =  get_size_dict(lowercase_)
     snake_case_	:      str            =  crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
     snake_case_	:      str            =  get_size_dict(lowercase_	,    param_name="crop_size")

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_ = PIL.Image.NEAREST	,    lowercase_ = None	,    **lowercase_	,    ):
     snake_case_	:      Dict            =  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     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_ = None	,    **lowercase_	,    ):
     snake_case_	:      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     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_ = True	,    lowercase_ = None	,    **lowercase_	,    ):
     if offset:
         snake_case_	:      Optional[int]            =  (image - 127.5) * scale
         if data_format is not None:
             snake_case_	:      str            =  to_channel_dimension_format(lowercase_	,    lowercase_)
         snake_case_	:      List[Any]            =  rescaled_image.astype(np.floataa)
     else:
         snake_case_	:      Tuple            =  rescale(lowercase_	,    scale=lowercase_	,    data_format=lowercase_	,    **lowercase_)
     return rescaled_image





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_ = None	,    **lowercase_	,    ):
     return normalize(lowercase_	,    mean=lowercase_	,    std=lowercase_	,    data_format=lowercase_	,    **lowercase_)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_=None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = ChannelDimension.FIRST	,    **lowercase_	,    ):
     snake_case_	:      List[Any]            =  do_resize if do_resize is not None else self.do_resize
     snake_case_	:      Optional[int]            =  resample if resample is not None else self.resample
     snake_case_	:      Dict            =  do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_	:      Any            =  do_rescale if do_rescale is not None else self.do_rescale
     snake_case_	:      str            =  rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_	:      Any            =  rescale_offset if rescale_offset is not None else self.rescale_offset
     snake_case_	:      List[Any]            =  do_normalize if do_normalize is not None else self.do_normalize
     snake_case_	:      Tuple            =  image_mean if image_mean is not None else self.image_mean
     snake_case_	:      List[Any]            =  image_std if image_std is not None else self.image_std
     snake_case_	:      List[Any]            =  include_top if include_top is not None else self.include_top

     snake_case_	:      str            =  size if size is not None else self.size
     snake_case_	:      int            =  get_size_dict(lowercase_)
     snake_case_	:      Optional[int]            =  crop_size if crop_size is not None else self.crop_size
     snake_case_	:      str            =  get_size_dict(lowercase_	,    param_name="crop_size")

     snake_case_	:      Union[str, Any]            =  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.
     snake_case_	:      Dict            =  [to_numpy_array(lowercase_) for image in images]

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

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

     if do_rescale:
         snake_case_	:      int            =  [self.rescale(image=lowercase_	,    scale=lowercase_	,    offset=lowercase_) for image in images]

     if do_normalize:
         snake_case_	:      int            =  [self.normalize(image=lowercase_	,    mean=lowercase_	,    std=lowercase_) for image in images]

     if include_top:
         snake_case_	:      Dict            =  [self.normalize(image=lowercase_	,    mean=[0, 0, 0]	,    std=lowercase_) for image in images]

     snake_case_	:      Optional[Any]            =  [to_channel_dimension_format(lowercase_	,    lowercase_) for image in images]

     snake_case_	:      int            =  {"pixel_values": images}
     return BatchFeature(data=lowercase_	,    tensor_type=lowercase_)






'''simple docstring'''
from math import exp

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    return 8 * x - 2 * exp(-x    )

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  lower_bound
    snake_case_	:      int            =  upper_bound
    for _ in range(0,						__SCREAMING_SNAKE_CASE    ):
        snake_case_     ,	snake_case_	:      List[str]            =  xa, xa - (f(__SCREAMING_SNAKE_CASE    ) * (xa - xa)) / (f(__SCREAMING_SNAKE_CASE    ) - f(__SCREAMING_SNAKE_CASE    ))
    return xa


if __name__ == "__main__":
  print(f"""Example: {secant_method(1, 3, 2)}""")




