
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

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				size if size is not None else {'''shortest_edge''': 2_5_6}
           UpperCamelCase__    :   Optional[Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   int					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						param_name='''crop_size'''  )

           UpperCamelCase__    :   Optional[int]					=				do_resize
           UpperCamelCase__    :   str					=				size
           UpperCamelCase__    :   Dict					=				do_center_crop
           UpperCamelCase__    :   Tuple					=				crop_size
           UpperCamelCase__    :   List[Any]					=				resample
           UpperCamelCase__    :   List[Any]					=				do_rescale
           UpperCamelCase__    :   Dict					=				rescale_factor
           UpperCamelCase__    :   Union[str, Any]					=				offset
           UpperCamelCase__    :   Tuple					=				do_normalize
           UpperCamelCase__    :   Dict					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__    :   str					=				image_std if image_std is not None else IMAGENET_STANDARD_STD






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           if "shortest_edge" in size:
                 UpperCamelCase__    :   Any					=				get_resize_output_image_size(__SCREAMING_SNAKE_CASE						,						size['''shortest_edge''']						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           elif "height" in size and "width" in size:
                 UpperCamelCase__    :   str					=				(size['''height'''], size['''width'''])
           else:
                 raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           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(__SCREAMING_SNAKE_CASE						,						size=(size['''height'''], size['''width'''])						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				image.astype(np.floataa  )
           if offset:
                 UpperCamelCase__    :   Any					=				image - (scale / 2)
           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						)  ->   np.ndarray:




           """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__    :   Optional[int]					=				to_numpy_array(__SCREAMING_SNAKE_CASE  )

           if do_resize:
                 UpperCamelCase__    :   Any					=				self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  )

           if do_center_crop:
                 UpperCamelCase__    :   Optional[Any]					=				self.center_crop(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE  )

           if do_rescale:
                 UpperCamelCase__    :   Union[str, Any]					=				self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						offset=__SCREAMING_SNAKE_CASE  )

           if do_normalize:
                 UpperCamelCase__    :   List[Any]					=				self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Any					=				to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           return image






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   Dict					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase__    :   str					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   int					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   Optional[Any]					=				offset if offset is not None else self.offset
           UpperCamelCase__    :   Dict					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   int					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   int					=				image_std if image_std is not None else self.image_std

           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else self.size
           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__    :   int					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						param_name='''crop_size'''  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

           UpperCamelCase__    :   str					=				make_batched(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[Any]					=				[
               [
                   self._preprocess_image(
                       image=__SCREAMING_SNAKE_CASE						,						do_resize=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						do_center_crop=__SCREAMING_SNAKE_CASE						,						crop_size=__SCREAMING_SNAKE_CASE						,						do_rescale=__SCREAMING_SNAKE_CASE						,						rescale_factor=__SCREAMING_SNAKE_CASE						,						offset=__SCREAMING_SNAKE_CASE						,						do_normalize=__SCREAMING_SNAKE_CASE						,						image_mean=__SCREAMING_SNAKE_CASE						,						image_std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						)
                   for img in video
               ]
               for video in videos
           ]

           UpperCamelCase__    :   List[Any]					=				{'''pixel_values''': videos}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE						,						tensor_type=__SCREAMING_SNAKE_CASE  )








from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          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      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


