


"""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       __A				:






        """simple docstring"""




        @staticmethod
        def       SCREAMING_SNAKE_CASE							(		*__A ,    **__A						)					->     Tuple:
            pass
def _A						(						lowercase		):







    """simple docstring"""
    a 		=hashlib.mda(image.tobytes()		)
    return m.hexdigest()






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






 """simple docstring"""




 __lowerCAmelCase 					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A						)					->     Union[str, Any]:
     a 		=DepthEstimationPipeline(model=__A ,    image_processor=__A						)
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     Union[str, Any]:
     a 		=depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     self.assertEqual({'''predicted_depth''': ANY(torch.Tensor						), '''depth''': ANY(Image.Image						)} ,    __A						)
     import datasets

     a 		=datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,    '''image''' ,    split='''test'''						)
     a 		=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						)},
         ] ,    __A ,    )


 @require_tf
 @unittest.skip('''Depth estimation is not implemented in TF'''						)
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     pass


 @slow
 @require_torch
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		='''Intel/dpt-large'''
     a 		=pipeline('''depth-estimation''' ,    model=__A						)
     a 		=depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     # This is highly irregular to have no small tests.
     self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''						)




"""simple docstring"""
def       _snake_case						(						UpperCamelCase						:		list						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						):
  raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""						)
 if len(UpperCamelCase						) == 0:
  raise ValueError("""Input list must be a non empty list"""						)
 if len(UpperCamelCase						) == 1:
  return True
 UpperCAmelCase	:					Union[str, Any]	=	series[1] - series[0]
 for index in range(len(UpperCamelCase						) - 1						):
  if series[index + 1] - series[index] != common_diff:
   return False
 return True

def       _snake_case						(						UpperCamelCase						:		list						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						):
  raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""						)
 if len(UpperCamelCase						) == 0:
  raise ValueError("""Input list must be a non empty list"""						)
 UpperCAmelCase	:					Any	=	0
 for val in series:
  answer += val
 return answer / len(UpperCamelCase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
