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		:
          @staticmethod
          def     UpperCamelCase_					(	*lowerCAmelCase			,			**lowerCAmelCase )							->       str:
           pass







def       A__   (						snake_case_			:		Image   ):
 SCREAMING_SNAKE_CASE__: Tuple=   hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		_lowerCamelCase		( unittest.TestCase     ):
    __a						=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: int=   DepthEstimationPipeline(model=lowerCAmelCase			,			image_processor=lowerCAmelCase )
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
     SCREAMING_SNAKE_CASE__: int=   depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )}			,			lowerCAmelCase )
     import datasets

     SCREAMING_SNAKE_CASE__: List[Any]=   datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,			'''image'''			,			split='''test''' )
     SCREAMING_SNAKE_CASE__: List[Any]=   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     UpperCamelCase_					(	self )							->       str:
     pass
    @slow
    @require_torch
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   '''Intel/dpt-large'''
     SCREAMING_SNAKE_CASE__: Any=   pipeline('''depth-estimation'''			,			model=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
     SCREAMING_SNAKE_CASE__: Any=   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     UpperCamelCase_					(	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_      (					__snake_case  :							int	,   __snake_case  :							int	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
   # formula for sum of series
   return total



def snake_case_      (					)			->					str:
   print(sum_of_series(1	,   1	,   10))


if __name__ == "__main__":
 import doctest

 doctest.testmod()





