




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a = True		,     _a = None		,     _a = 32		,     _a = True		,     _a = 1 / 255		,     _a = True		,     _a = True		,     _a = [0.48145466, 0.4578275, 0.40821073]		,     _a = [0.26862954, 0.26130258, 0.27577711]		,     _a = True		,     _a=7		,     _a=30		,     _a=400		,     _a=3		,     )						->							List[Any]:
           _A							:     List[str]			=						parent
           _A							:     Any			=						do_resize
           _A							:     Tuple			=						size if size is not None else {"""shortest_edge""": 288}
           _A							:     Dict			=						size_divisor
           _A							:     Optional[Any]			=						do_rescale
           _A							:     int			=						rescale_factor
           _A							:     Dict			=						do_normalize
           _A							:     Union[str, Any]			=						do_center_crop
           _A							:     Any			=						image_mean
           _A							:     int			=						image_std
           _A							:     Optional[Any]			=						do_pad
           _A							:     List[str]			=						batch_size
           _A							:     Tuple			=						num_channels
           _A							:     Optional[int]			=						min_resolution
           _A							:     int			=						max_resolution

     def 					a__	(  self				)						->							List[str]:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }



     def 					a__	(  self		,     _a		,     _a=False				)						->							Optional[int]:
           if not batched:
                 _A							:     Optional[int]			=						self.size["""shortest_edge"""]
                 _A							:     Tuple			=						image_inputs[0]
                 if isinstance(_a		,     Image.Image				):
                       _A			,   _A							:     Dict			=						image.size
                 else:
                       _A			,   _A							:     Union[str, Any]			=						image.shape[1], image.shape[2]
                 _A							:     int			=						size / min(_a		,     _a				)
                 if h < w:
                       _A			,   _A							:     int			=						size, scale * w
                 else:
                       _A			,   _A							:     Dict			=						scale * h, size

                 _A							:     int			=						int((1333 / 800) * size				)
                 if max(_a		,     _a				) > max_size:
                       _A							:     Union[str, Any]			=						max_size / max(_a		,     _a				)
                       _A							:     Any			=						newh * scale
                       _A							:     Union[str, Any]			=						neww * scale

                 _A			,   _A							:     str			=						int(newh + 0.5				), int(neww + 0.5				)
                 _A			,   _A							:     Any			=						(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 _A							:     Dict			=						[]
                 for image in image_inputs:
                       _A			,   _A							:     Dict			=						self.get_expected_values([image]				)
                       expected_values.append((expected_height, expected_width)				)
                 _A							:     List[Any]			=						max(_a		,     key=lambda _a				: item[0]				)[0]
                 _A							:     Dict			=						max(_a		,     key=lambda _a				: item[1]				)[1]

           return expected_height, expected_width







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						BridgeTowerImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Any:
           _A							:     int			=						BridgeTowerImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							List[str]:
           _A							:     int			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """size_divisor"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           pass

     def 					a__	(  self				)						->							Tuple:
           # Initialize image processor
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     Optional[Any]			=						self.image_processor_tester.get_expected_values(_a				)
           self.assertEqual(
               encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

           # Test batched
           _A							:     Tuple			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     int			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,     )

     def 					a__	(  self				)						->							Tuple:
           # Initialize image processor
           _A							:     Dict			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     str			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     Dict			=						self.image_processor_tester.get_expected_values(_a				)
           self.assertEqual(
               encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

           # Test batched
           _A							:     List[Any]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     Tuple			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,     )



     def 					a__	(  self				)						->							Any:
           # Initialize image processor
           _A							:     Optional[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Any			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     Optional[int]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     Any			=						self.image_processor_tester.get_expected_values(_a				)
           self.assertEqual(
               encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

           # Test batched
           _A							:     Optional[Any]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     str			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )		,     )







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

_snake_case										=       6_3_7_8_1_3_7.0
_snake_case										=       6_3_5_6_7_5_2.3_1_4_2_4_5
_snake_case										=       6378137







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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

