





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__A				:    List[str]       =   logging.get_logger(__name__)





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[List[ImageInput]]:







			'''simple docstring'''

			if isinstance(UpperCamelCase__			,			(list, tuple) ) and isinstance(videos[0]			,			(list, tuple) ) and is_valid_image(videos[0][0] ):
						return videos

			elif isinstance(UpperCamelCase__			,			(list, tuple) ) and is_valid_image(videos[0] ):
						return [videos]

			elif is_valid_image(UpperCamelCase__ ):
						return [[videos]]

			raise ValueError(F"""Could not make batched video from {videos}""" )






class 	A_       (a_			):
	UpperCAmelCase__					=			['''pixel_values''']
	def __init__(    self							,					_A = True							,					_A = None							,					_A = PILImageResampling.BILINEAR							,					_A = True							,					_A = None							,					_A = True							,					_A = 1 / 2_5_5							,					_A = True							,					_A = True							,					_A = None							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(**_A		)
				UpperCAmelCase   		=				size if size is not None else {'''shortest_edge''': 2_5_6}
				UpperCAmelCase   		=				get_size_dict(_A							,					default_to_square=_A		)
				UpperCAmelCase   		=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				UpperCAmelCase   		=				get_size_dict(_A							,					param_name='''crop_size'''		)

				UpperCAmelCase   		=				do_resize
				UpperCAmelCase   		=				size
				UpperCAmelCase   		=				do_center_crop
				UpperCAmelCase   		=				crop_size
				UpperCAmelCase   		=				resample
				UpperCAmelCase   		=				do_rescale
				UpperCAmelCase   		=				rescale_factor
				UpperCAmelCase   		=				offset
				UpperCAmelCase   		=				do_normalize
				UpperCAmelCase   		=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				UpperCAmelCase   		=				image_std if image_std is not None else IMAGENET_STANDARD_STD
	def      _lowercase				(    self							,					_A							,					_A							,					_A = PILImageResampling.BILINEAR							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				get_size_dict(_A							,					default_to_square=_A		)
				if "shortest_edge" in size:
							UpperCAmelCase   		=				get_resize_output_image_size(_A							,					size['''shortest_edge''']							,					default_to_square=_A		)
				elif "height" in size and "width" in size:
							UpperCAmelCase   		=				(size['''height'''], size['''width'''])
				else:
							raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
				return resize(_A							,					size=_A							,					resample=_A							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				get_size_dict(_A		)
				if "height" not in size or "width" not in size:
							raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
				return center_crop(_A							,					size=(size['''height'''], size['''width'''])							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A = True							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				image.astype(np.floataa		)
				if offset:
							UpperCAmelCase   		=				image - (scale / 2)
				return rescale(_A							,					scale=_A							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				return normalize(_A							,					mean=_A							,					std=_A							,					data_format=_A							,					**_A		)
	def      _lowercase				(    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 = ChannelDimension.FIRST							,					):






				'''simple docstring'''







				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.'''		)

				if offset and not do_rescale:
							raise ValueError('''For offset, do_rescale must also be set to True.'''		)

				# All transformations expect numpy arrays.
				UpperCAmelCase   		=				to_numpy_array(_A		)

				if do_resize:
							UpperCAmelCase   		=				self.resize(image=_A							,					size=_A							,					resample=_A		)

				if do_center_crop:
							UpperCAmelCase   		=				self.center_crop(_A							,					size=_A		)

				if do_rescale:
							UpperCAmelCase   		=				self.rescale(image=_A							,					scale=_A							,					offset=_A		)

				if do_normalize:
							UpperCAmelCase   		=				self.normalize(image=_A							,					mean=_A							,					std=_A		)

				UpperCAmelCase   		=				to_channel_dimension_format(_A							,					_A		)
				return image







	def      _lowercase				(    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   		=				do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase   		=				resample if resample is not None else self.resample
				UpperCAmelCase   		=				do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase   		=				do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase   		=				rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase   		=				offset if offset is not None else self.offset
				UpperCAmelCase   		=				do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase   		=				image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase   		=				image_std if image_std is not None else self.image_std

				UpperCAmelCase   		=				size if size is not None else self.size
				UpperCAmelCase   		=				get_size_dict(_A							,					default_to_square=_A		)
				UpperCAmelCase   		=				crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase   		=				get_size_dict(_A							,					param_name='''crop_size'''		)

				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.'''		)

				UpperCAmelCase   		=				make_batched(_A		)

				UpperCAmelCase   		=				[
				    [
				        self._preprocess_image(
				            image=_A							,					do_resize=_A							,					size=_A							,					resample=_A							,					do_center_crop=_A							,					crop_size=_A							,					do_rescale=_A							,					rescale_factor=_A							,					offset=_A							,					do_normalize=_A							,					image_mean=_A							,					image_std=_A							,					data_format=_A							,					)
				        for img in video
				    ]
				    for video in videos
				]

				UpperCAmelCase   		=				{'''pixel_values''': videos}
				return BatchFeature(data=_A							,					tensor_type=_A		)










from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							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:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



