





'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class     __lowerCAmelCase					:






          """simple docstring"""





          @staticmethod
          def snake_case__						(							*lowerCAmelCase__       :		Tuple	,			**lowerCAmelCase__       :		Any					)   ->					List[str]:

               '''simple docstring'''




               pass







def  a__    (    lowercase :					Image				)		->		str:





     """simple docstring"""

     _UpperCamelCase       =	hashlib.mda(image.tobytes()				)
     return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					str						    =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	DepthEstimationPipeline(model=lowerCAmelCase__	,			image_processor=lowerCAmelCase__					)
            return depth_estimator, [
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
            ]





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		List[Any]					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
            self.assertEqual({'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)}	,			lowerCAmelCase__					)
            import datasets

            _UpperCamelCase       =	datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''	,			'''image'''	,			split='''test'''					)
            _UpperCamelCase       =	depth_estimator(
                [
                    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
                    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    # RGBA
                    dataset[0]['''file'''],
                    # LA
                    dataset[1]['''file'''],
                    # L
                    dataset[2]['''file'''],
                ]					)
            self.assertEqual(
                [
                    {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
                    {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
                    {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
                    {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
                    {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
                ]	,			lowerCAmelCase__	,			)





       @require_tf
       @unittest.skip('''Depth estimation is not implemented in TF'''					)
       def snake_case__						(							self       :		Optional[int]					)   ->					int:

            '''simple docstring'''




            pass





       @slow
       @require_torch
       def snake_case__						(							self       :		Optional[Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	'''Intel/dpt-large'''
            _UpperCamelCase       =	pipeline('''depth-estimation'''	,			model=lowerCAmelCase__					)
            _UpperCamelCase       =	depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
            _UpperCamelCase       =	hashimage(outputs['''depth''']					)

            # This seems flaky.
            # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
            self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()					)	,			29.304					)
            self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()					)	,			2.662					)







       @require_torch
       def snake_case__						(							self       :		Any					)   ->					Union[str, Any]:

            '''simple docstring'''




            self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''					)










'''simple docstring'''

import math







def  a__    (    lowercase :					float, lowercase :					float				)		->		float:





     """simple docstring"""

     if initial_intensity < 0:
          raise ValueError('''The value of intensity cannot be negative'''				)
          # handling of negative values of initial intensity
     if angle < 0 or angle > 360:
          raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees'''				)
          # handling of values out of allowed range
     return initial_intensity * (math.cos(math.radians(lowercase				)				) ** 2)


if __name__ == "__main__":
   import doctest

   doctest.testmod(name='malus_law')



