
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 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, logging


UpperCamelCase_  =	logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     ['pixel_values']





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


								"""simple docstring"""

								super().__init__(**snake_case_							)
								A     :  int   =				size if size is not None else {'''height''': 224, '''width''': 224}
								A     :  str   =				get_size_dict(snake_case_							)
								A     :  Any   =				do_resize
								A     :  Union[str, Any]   =				do_rescale
								A     :  str   =				do_normalize
								A     :  int   =				size
								A     :  int   =				resample
								A     :  List[str]   =				rescale_factor
								A     :  str   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								A     :  str   =				image_std if image_std is not None else IMAGENET_STANDARD_STD





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		np.ndarray					,      snake_case_ :		Dict[str, int]					,      snake_case_ :		PILImageResampling = PILImageResampling.BILINEAR					,      snake_case_ :		Optional[Union[str, ChannelDimension]] = None					,      **snake_case_ :		str					,      ):


								"""simple docstring"""

								A     :  List[str]   =				get_size_dict(snake_case_							)
								if "height" not in size or "width" not in size:
												raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
								return resize(
								    snake_case_					,      size=(size['''height'''], size['''width'''])					,      resample=snake_case_					,      data_format=snake_case_					,      **snake_case_							)





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


								"""simple docstring"""

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





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


								"""simple docstring"""

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






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


								"""simple docstring"""

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

								A     :  str   =				size if size is not None else self.size
								A     :  List[Any]   =				get_size_dict(snake_case_							)

								A     :  List[str]   =				make_list_of_images(snake_case_							)

								if not valid_images(snake_case_							):
												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_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

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

								if do_resize:
												A     :  Any   =				[self.resize(image=snake_case_					,      size=snake_case_					,      resample=snake_case_							) for image in images]

								if do_rescale:
												A     :  List[str]   =				[self.rescale(image=snake_case_					,      scale=snake_case_							) for image in images]

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

								A     :  Tuple   =				[to_channel_dimension_format(snake_case_					,      snake_case_							) for image in images]

								A     :  List[Any]   =				{'''pixel_values''': images}
								return BatchFeature(data=snake_case_					,      tensor_type=snake_case_							)

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()