





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        SCREAMING_SNAKE_CASE     :


         @staticmethod
         def  SCREAMING_SNAKE_CASE_							(					*a    :       List[Any]    ,  **a    :       Optional[Any] )->     Union[str, Any]:





           """simple docstring"""







           pass




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__			    =						hashlib.mda(image.tobytes()	)
  return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   _UpperCamelCase					:	Union[str, Any]				      = MODEL_FOR_DEPTH_ESTIMATION_MAPPING


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Tuple    ,  a    :       Optional[int]    ,  a    :       Optional[int] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						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    :       Optional[int]    ,  a    :       str    ,  a    :       Optional[Any] )->     str:





     """simple docstring"""







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

     lowercase__			    =						datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,  'image'    ,  split='test' )
     lowercase__			    =						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    :       Any )->     str:





     """simple docstring"""







     pass


   @slow
   @require_torch
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						"""Intel/dpt-large"""
     lowercase__			    =						pipeline('depth-estimation'    ,  model=_a )
     lowercase__			    =						depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg' )
     lowercase__			    =						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[str] )->     int:





     """simple docstring"""







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









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = 50	)       ->     int:
  lowercase__			    =						[[0] * 3 for _ in range(length + 1	)]

  for row_length in range(length + 1	):
    for tile_length in range(2	,		5	):
      for tile_start in range(row_length - tile_length + 1	):
        different_colour_ways_number[row_length][tile_length - 2] += (
            different_colour_ways_number[row_length - tile_start - tile_length][
                tile_length - 2
            ]
            + 1
        )

  return sum(different_colour_ways_number[length]	)


if __name__ == "__main__":
      print(f'''{solution() = }''')


