
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


snake_case :		Optional[int]							      =  logging.get_logger(__name__)


if is_vision_available():
				import PIL





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    ['pixel_values']







			def __init__(				self						,   _a = True						,   _a = None						,   _a = PILImageResampling.BICUBIC						,   _a = True						,   _a = None						,   _a = True						,   _a = 1 / 255						,   _a = True						,   _a = None						,   _a = None						,   _a = True						,   **_a						,   ):
				super().__init__(**_a		)
				__magic_name__		:		Tuple	   =     size if size is not None else {"shortest_edge": 224}
				__magic_name__		:		int	   =     get_size_dict(_a						,   default_to_square=_a		)
				__magic_name__		:		Dict	   =     crop_size if crop_size is not None else {"height": 224, "width": 224}
				__magic_name__		:		Tuple	   =     get_size_dict(_a						,   default_to_square=_a						,   param_name="crop_size"		)

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = PILImageResampling.BICUBIC						,   _a = None						,   **_a						,   ):
				__magic_name__		:		List[Any]	   =     get_size_dict(_a						,   default_to_square=_a		)
				if "shortest_edge" not in size:
					raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
				__magic_name__		:		Dict	   =     get_resize_output_image_size(_a						,   size=size["shortest_edge"]						,   default_to_square=_a		)
				return resize(_a						,   size=_a						,   resample=_a						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				__magic_name__		:		Union[str, Any]	   =     get_size_dict(_a		)
				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(_a						,   size=(size["height"], size["width"])						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				return rescale(_a						,   scale=_a						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				return normalize(_a						,   mean=_a						,   std=_a						,   data_format=_a						,   **_a		)







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

				__magic_name__		:		List[Any]	   =     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:
					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:
					__magic_name__		:		Optional[Any]	   =     [convert_to_rgb(_a		) for image in images]

				# All transformations expect numpy arrays.
				__magic_name__		:		Union[str, Any]	   =     [to_numpy_array(_a		) for image in images]

				if do_resize:
					__magic_name__		:		List[Any]	   =     [self.resize(image=_a						,   size=_a						,   resample=_a		) for image in images]

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

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

				if do_normalize:
					__magic_name__		:		List[str]	   =     [self.normalize(image=_a						,   mean=_a						,   std=_a		) for image in images]

				__magic_name__		:		Dict	   =     [to_channel_dimension_format(_a						,   _a		) for image in images]

				__magic_name__		:		Any	   =     {"pixel_values": images}
				return BatchFeature(data=_a						,   tensor_type=_a		)


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


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

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


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

				testmod()
