




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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case            = logging.get_logger(__name__)







class 		lowercase							(     UpperCamelCase__	):
     _a											=		["pixel_values"]

     def __init__(						self	,  _a = True	,  _a = None	,  _a = PILImageResampling.BILINEAR	,  _a = True	,  _a = None	,  _a = True	,  _a = 1 / 255	,  _a = True	,  _a = None	,  _a = None	,  **_a	,  )		->							None:
           super().__init__(**_a       )
           _A							:  Optional[Any]    =		size if size is not None else {"""shortest_edge""": 256}
           _A							:  List[str]    =		get_size_dict(_a	,  default_to_square=_a       )
           _A							:  Tuple    =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           _A							:  int    =		get_size_dict(_a       )
           _A							:  Union[str, Any]    =		do_resize
           _A							:  Union[str, Any]    =		size
           _A							:  Union[str, Any]    =		resample
           _A							:  Dict    =		do_center_crop
           _A							:  str    =		crop_size
           _A							:  Dict    =		do_rescale
           _A							:  Tuple    =		rescale_factor
           _A							:  Any    =		do_normalize
           _A							:  Any    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _A							:  Optional[int]    =		image_std if image_std is not None else IMAGENET_STANDARD_STD

     def       a__			(						self	,  _a	,  _a	,  _a = PILImageResampling.BICUBIC	,  _a = None	,  **_a	,  )		->							np.ndarray:
           _A							:  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()}'''       )
           _A							:  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       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
           _A							:  Union[str, Any]    =		get_size_dict(_a       )
           return center_crop(_a	,  size=(size["""height"""], size["""width"""])	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a       )		->							np.ndarray:
           return rescale(_a	,  scale=_a	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
           return normalize(_a	,  mean=_a	,  std=_a	,  data_format=_a	,  **_a       )



     def       a__			(						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	,  **_a	,  )		->							List[str]:
           _A							:  Dict    =		do_resize if do_resize is not None else self.do_resize
           _A							:  Any    =		size if size is not None else self.size
           _A							:  List[str]    =		get_size_dict(_a	,  default_to_square=_a       )
           _A							:  Optional[int]    =		resample if resample is not None else self.resample
           _A							:  int    =		do_center_crop if do_center_crop is not None else self.do_center_crop
           _A							:  str    =		crop_size if crop_size is not None else self.crop_size
           _A							:  List[Any]    =		get_size_dict(_a       )
           _A							:  Tuple    =		do_rescale if do_rescale is not None else self.do_rescale
           _A							:  Union[str, Any]    =		rescale_factor if rescale_factor is not None else self.rescale_factor
           _A							:  Any    =		do_normalize if do_normalize is not None else self.do_normalize
           _A							:  Optional[Any]    =		image_mean if image_mean is not None else self.image_mean
           _A							:  List[Any]    =		image_std if image_std is not None else self.image_std

           _A							:  Any    =		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."""       )

           # All transformations expect numpy arrays.
           _A							:  int    =		[to_numpy_array(_a       ) for image in images]

           if do_resize:
                 _A							:  List[str]    =		[self.resize(image=_a	,  size=_a	,  resample=_a       ) for image in images]

           if do_center_crop:
                 _A							:  str    =		[self.center_crop(image=_a	,  size=_a       ) for image in images]

           if do_rescale:
                 _A							:  Optional[int]    =		[self.rescale(image=_a	,  scale=_a       ) for image in images]

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

           _A							:  str    =		[to_channel_dimension_format(_a	,  _a       ) for image in images]

           _A							:  Optional[Any]    =		{"""pixel_values""": images}
           return BatchFeature(data=_a	,  tensor_type=_a       )







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> bool:
            _A							:  List[str]    =		sqrt((x**2) + (y**2)  )
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      _A							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		proportion * 4
      print(f'''The estimated value of pi is {pi_estimate}'''  )
      print(f'''The numpy value of pi is {pi}'''  )
      print(f'''The total error is {abs(pi - pi_estimate  )}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(max_value * max_value - min_value * min_value) / 2

      print("""******************"""  )
      print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {expected_value}'''  )
      print(f'''Total error is {abs(estimated_value - expected_value  )}'''  )
      print("""******************"""  )







def        lowerCAmelCase_			(  snake_case_  ):

      def function_to_integrate(snake_case_  ) -> float:
            return sqrt(4.0 - x * x  )

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,0.0,2.0  )

      print("""******************"""  )
      print("""Estimating pi using area_under_curve_estimator"""  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {pi}'''  )
      print(f'''Total error is {abs(estimated_value - pi  )}'''  )
      print("""******************"""  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

