

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["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_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Any 		=    size if size is not None else {"shortest_edge": 224}
										_UpperCAmelCase       :		List[str] 		=    get_size_dict(A_						,					default_to_square=A_	)
										_UpperCAmelCase       :		List[str] 		=    crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCAmelCase       :		List[str] 		=    get_size_dict(A_						,					default_to_square=A_						,					param_name="crop_size"	)

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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BICUBIC						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    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()}'	)
										_UpperCAmelCase       :		int 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       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_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Dict 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Union[str, Any] 		=    get_size_dict(A_						,					param_name="size"						,					default_to_square=A_	)
										_UpperCAmelCase       :		Any 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Union[str, Any] 		=    do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase       :		str 		=    crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_						,					param_name="crop_size"						,					default_to_square=A_	)
										_UpperCAmelCase       :		Dict 		=    do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase       :		Dict 		=    rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase       :		Optional[Any] 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Dict 		=    image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase       :		Optional[int] 		=    image_std if image_std is not None else self.image_std
										_UpperCAmelCase       :		Optional[Any] 		=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_UpperCAmelCase       :		Dict 		=    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:
														_UpperCAmelCase       :		List[Any] 		=    [convert_to_rgb(A_	) for image in images]

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

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

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

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

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

										_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		List[Any] 		=    {"pixel_values": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


