

'''simple docstring'''






# using dfs for finding eulerian path traversal


def 	_A  (					A__  ,       A__  ,       A__  ,       A__=None   ):






 """simple docstring"""



 __lowercase													=				(path or []) + [u]
 for v in graph[u]:
  if visited_edge[u][v] is False:
   __lowercase						,			__lowercase													=				True, True
   __lowercase													=				dfs(A__  ,       A__  ,       A__  ,       A__   )
 return path


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				0
 __lowercase													=				-1
 for i in range(A__   ):
  if i not in graph.keys():
   continue
  if len(graph[i]   ) % 2 == 1:
   odd_degree_nodes += 1
   __lowercase													=				i
 if odd_degree_nodes == 0:
  return 1, odd_node
 if odd_degree_nodes == 2:
  return 2, odd_node
 return 3, odd_node


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[[False for _ in range(max_node + 1   )] for _ in range(max_node + 1   )]
 __lowercase						,			__lowercase													=				check_circuit_or_path(A__  ,       A__   )
 if check == 3:
  print('''graph is not Eulerian'''   )
  print('''no path'''   )
  return
 __lowercase													=				1
 if check == 2:
  __lowercase													=				odd_node
  print('''graph has a Euler path'''   )
 if check == 1:
  print('''graph has a Euler cycle'''   )
 __lowercase													=				dfs(A__  ,       A__  ,       A__   )
 print(A__   )


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
 __lowercase													=				{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
 __lowercase													=				{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
 __lowercase													=				{1: [2, 3], 2: [1, 3], 3: [1, 2]}
 __lowercase													=				{
     1: [],
     2: []
     # all degree is zero
 }
 __lowercase													=				10
 check_euler(A__  ,       A__   )
 check_euler(A__  ,       A__   )
 check_euler(A__  ,       A__   )
 check_euler(A__  ,       A__   )
 check_euler(A__  ,       A__   )


if __name__ == "__main__":
 main()




'''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     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



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






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   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    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __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()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @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'''    )

