


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

SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)




def  a   (lowerCAmelCase__   ):
   if isinstance(lowerCAmelCase__		,				(list, tuple)   ) and isinstance(videos[0]		,				(list, tuple)   ) and is_valid_image(videos[0][0]   ):
      return videos

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

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

   raise ValueError(f'''Could not make batched video from {videos}'''   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""pixel_values"""]


     def __init__(	self  ,       __A = True  ,       __A = None  ,       __A = PILImageResampling.BILINEAR  ,       __A = True  ,       __A = None  ,       __A = True  ,       __A = 1 / 255  ,       __A = True  ,       __A = True  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        super().__init__(**__A    )
        __a					=  size if size is not None else {"""shortest_edge""": 256}
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __a					=  get_size_dict(__A  ,       param_name="""crop_size"""    )

        __a					=  do_resize
        __a					=  size
        __a					=  do_center_crop
        __a					=  crop_size
        __a					=  resample
        __a					=  do_rescale
        __a					=  rescale_factor
        __a					=  offset
        __a					=  do_normalize
        __a					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a					=  image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = PILImageResampling.BILINEAR  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        if "shortest_edge" in size:
           __a					=  get_resize_output_image_size(__A  ,       size["""shortest_edge"""]  ,       default_to_square=__A    )
        elif "height" in size and "width" in size:
           __a					=  (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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        __a					=  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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = True  ,       __A = None  ,       **__A  ,       ):
        __a					=  image.astype(np.floataa    )
        if offset:
           __a					=  image - (scale / 2)
        return rescale(__A  ,       scale=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        return normalize(__A  ,       mean=__A  ,       std=__A  ,       data_format=__A  ,       **__A    )


     def 	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 = ChannelDimension.FIRST  ,       ):
        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.
        __a					=  to_numpy_array(__A    )

        if do_resize:
           __a					=  self.resize(image=__A  ,       size=__A  ,       resample=__A    )

        if do_center_crop:
           __a					=  self.center_crop(__A  ,       size=__A    )

        if do_rescale:
           __a					=  self.rescale(image=__A  ,       scale=__A  ,       offset=__A    )

        if do_normalize:
           __a					=  self.normalize(image=__A  ,       mean=__A  ,       std=__A    )

        __a					=  to_channel_dimension_format(__A  ,       __A    )
        return image


     def 	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  ,       ):
        __a					=  do_resize if do_resize is not None else self.do_resize
        __a					=  resample if resample is not None else self.resample
        __a					=  do_center_crop if do_center_crop is not None else self.do_center_crop
        __a					=  do_rescale if do_rescale is not None else self.do_rescale
        __a					=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a					=  offset if offset is not None else self.offset
        __a					=  do_normalize if do_normalize is not None else self.do_normalize
        __a					=  image_mean if image_mean is not None else self.image_mean
        __a					=  image_std if image_std is not None else self.image_std

        __a					=  size if size is not None else self.size
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  crop_size if crop_size is not None else self.crop_size
        __a					=  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."""    )

        __a					=  make_batched(__A    )

        __a					=  [
            [
                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
        ]

        __a					=  {"""pixel_values""": videos}
        return BatchFeature(data=__A  ,       tensor_type=__A    )





"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a = 100,				):
		SCREAMING_SNAKE_CASE_    			=			x_start
		SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
		SCREAMING_SNAKE_CASE_    			=			0.0
		for _ in range(__a     ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				SCREAMING_SNAKE_CASE_    			=			(x_end - x_start) / steps + xa
				SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
				area += abs(fxa + fxa     ) * (xa - xa) / 2
				# Increment step
				SCREAMING_SNAKE_CASE_    			=			xa
				SCREAMING_SNAKE_CASE_    			=			fxa
		return area


if __name__ == "__main__":



						def 							_lowerCamelCase			(			__a     ):
								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:')
						lowerCAmelCase__				=					10
						while i <= 100000:
												print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
												i *= 10