



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						(							*__UpperCamelCase      :  Optional[int]						, **__UpperCamelCase      :  Optional[int]       )		->     Tuple:
															pass






def  lowerCamelCase_	(      lowerCAmelCase__	:	Image				)       ->							str:

							'''simple docstring'''






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



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):
				A_   :     str								=     MODEL_FOR_DEPTH_ESTIMATION_MAPPING


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  int       )		->     Union[str, Any]:
											A      						=    DepthEstimationPipeline(model=__UpperCamelCase						, image_processor=__UpperCamelCase       )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  str       )		->     Dict:
											A      						=    depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
											self.assertEqual({'predicted_depth': ANY(torch.Tensor       ), 'depth': ANY(Image.Image       )}						, __UpperCamelCase       )
											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       )},
											    ]						, __UpperCamelCase						, )


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


				@slow
				@require_torch
				def __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    '''Intel/dpt-large'''
											A      						=    pipeline('depth-estimation'						, model=__UpperCamelCase       )
											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()       )						, 2.6_6_2       )


				@require_torch
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     int:
											self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'       )







'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



