
"""simple docstring"""

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, 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, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Dict				   =						["pixel_values"]




      def __init__(				self ,							lowercase_ = True ,							lowercase_ = None ,							lowercase_ = PIL.Image.BICUBIC ,							lowercase_ = True ,							lowercase_ = None ,							lowercase_ = 1 / 2_5_5 ,							lowercase_ = True ,							lowercase_ = True ,							lowercase_ = None ,							lowercase_ = None ,							**lowercase_ ,							)    ->       None:
          super().__init__(**lowercase_				)
          UpperCAmelCase          =       size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
          UpperCAmelCase          =       get_size_dict(lowercase_				)
          UpperCAmelCase          =       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          UpperCAmelCase          =       get_size_dict(lowercase_ ,							param_name='crop_size'				)

          UpperCAmelCase          =       do_resize
          UpperCAmelCase          =       size
          UpperCAmelCase          =       resample
          UpperCAmelCase          =       do_center_crop
          UpperCAmelCase          =       crop_size
          UpperCAmelCase          =       do_rescale
          UpperCAmelCase          =       rescale_factor
          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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ = PIL.Image.BICUBIC ,							lowercase_ = None ,							**lowercase_ ,							)    ->       np.ndarray:
          UpperCAmelCase          =       get_size_dict(lowercase_				)
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
          return resize(
              lowercase_ ,							size=(size['height'], size['width']) ,							resample=lowercase_ ,							data_format=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ = None ,							**lowercase_ ,							)    ->       np.ndarray:
          UpperCAmelCase          =       get_size_dict(lowercase_				)
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
          return center_crop(lowercase_ ,							size=(size['height'], size['width']) ,							data_format=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ = None ,							**lowercase_ ,							)    ->       Dict:
          return rescale(lowercase_ ,							scale=lowercase_ ,							data_format=lowercase_ ,							**lowercase_				)




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




      def      a_   (				self ,							lowercase_ ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_ = ChannelDimension.FIRST ,							**lowercase_ ,							)    ->       PIL.Image.Image:
          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          =       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(lowercase_				)
          UpperCAmelCase          =       crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =       get_size_dict(lowercase_ ,							param_name='crop_size'				)

          UpperCAmelCase          =       make_list_of_images(lowercase_				)

          if not valid_images(lowercase_				):
              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 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.'				)

          # All transformations expect numpy arrays.
          UpperCAmelCase          =       [to_numpy_array(lowercase_				) for image in images]

          if do_resize:
              UpperCAmelCase          =       [self.resize(image=lowercase_ ,							size=lowercase_ ,							resample=lowercase_				) for image in images]

          if do_center_crop:
              UpperCAmelCase          =       [self.center_crop(image=lowercase_ ,							size=lowercase_				) for image in images]

          if do_rescale:
              UpperCAmelCase          =       [self.rescale(image=lowercase_ ,							scale=lowercase_				) for image in images]

          if do_normalize:
              UpperCAmelCase          =       [self.normalize(image=lowercase_ ,							mean=lowercase_ ,							std=lowercase_				) for image in images]

          UpperCAmelCase          =       [to_channel_dimension_format(lowercase_ ,							lowercase_				) for image in images]

          UpperCAmelCase          =       {'pixel_values': images}
          return BatchFeature(data=lowercase_ ,							tensor_type=lowercase_				)





"""simple docstring"""

from math import asin, atan, cos, radians, sin, sqrt, tan

SCREAMING_SNAKE_CASE_            =    6_3_7_8_1_3_7.0
SCREAMING_SNAKE_CASE_            =    6_3_5_6_7_5_2.3_1_4_2_4_5
SCREAMING_SNAKE_CASE_            =    637_8137




def      lowercase__    (       lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			,					lowerCAmelCase					:       float			)			->   float:






    """simple docstring"""




    UpperCAmelCase          =       (AXIS_A - AXIS_B) / AXIS_A
    UpperCAmelCase          =       atan((1 - flattening) * tan(radians(lowerCAmelCase			)			)			)
    UpperCAmelCase          =       atan((1 - flattening) * tan(radians(lowerCAmelCase			)			)			)
    UpperCAmelCase          =       radians(lowerCAmelCase			)
    UpperCAmelCase          =       radians(lowerCAmelCase			)
    # Equation
    UpperCAmelCase          =       sin((phi_a - phi_a) / 2			)
    UpperCAmelCase          =       sin((lambda_a - lambda_a) / 2			)
    # Square both values
    sin_sq_phi *= sin_sq_phi
    sin_sq_lambda *= sin_sq_lambda
    UpperCAmelCase          =       sqrt(sin_sq_phi + (cos(lowerCAmelCase			) * cos(lowerCAmelCase			) * sin_sq_lambda)			)
    return 2 * RADIUS * asin(lowerCAmelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



