


'''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 				lowerCamelCase__				(       *snake_case  :	Dict							,				**snake_case  :	Optional[int]    )		->							List[str]:
                pass




def 			_a			(		_lowercase      :			Image						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    hashlib.mda(image.tobytes()						)
     return m.hexdigest()



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


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Any							     =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	List[str]    )		->							Dict:
          __UpperCAmelCase			:				Tuple													=    DepthEstimationPipeline(model=snake_case							,				image_processor=snake_case    )
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Optional[Any]							,				snake_case  :	int    )		->							Dict:
          __UpperCAmelCase			:				str													=    depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
          self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}							,				snake_case    )
          import datasets

          __UpperCAmelCase			:				int													=    datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''							,				'''image'''							,				split='''test'''    )
          __UpperCAmelCase			:				Optional[int]													=    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    )},
              ]							,				snake_case							,				)

     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[Any]:
          pass

     @slow
     @require_torch
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
          __UpperCAmelCase			:				Optional[Any]													=    '''Intel/dpt-large'''
          __UpperCAmelCase			:				Optional[Any]													=    pipeline('''depth-estimation'''							,				model=snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
          __UpperCAmelCase			:				Dict													=    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 				lowerCamelCase__				(       self  :	List[str]    )		->							Tuple:
          # 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 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()