



"""simple docstring"""

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

lowercase__					:						Any												=			6378137.0
lowercase__					:						str												=			6356752.314245
lowercase__					:						Tuple												=			6_37_81_37




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Any       =			(AXIS_A - AXIS_B) / AXIS_A
    snake_case_				:  int       =			atan((1 - flattening) * tan(radians(_a					)					)					)
    snake_case_				:  Optional[int]       =			atan((1 - flattening) * tan(radians(_a					)					)					)
    snake_case_				:  int       =			radians(_a					)
    snake_case_				:  int       =			radians(_a					)
    # Equation
    snake_case_				:  Dict       =			sin((phi_a - phi_a) / 2					)
    snake_case_				:  Union[str, Any]       =			sin((lambda_a - lambda_a) / 2					)
    # Square both values
    sin_sq_phi *= sin_sq_phi
    sin_sq_lambda *= sin_sq_lambda
    snake_case_				:  Optional[Any]       =			sqrt(sin_sq_phi + (cos(_a					) * cos(_a					) * sin_sq_lambda)					)
    return 2 * RADIUS * asin(_a					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





