




import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch




@require_torch
@require_optimum
@slow
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			"hf-internal-testing/tiny-random-t5"
							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

							a    =			tokenizer("This is me"					,      return_tensors="pt"							)

							a    =			model.to_bettertransformer()

							self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							a    =			model.generate(**__lowerCamelCase							)

							a    =			model.reverse_bettertransformer()

							self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__lowerCamelCase							)

										a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

										self.assertFalse(
										    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

										a    =			model_reloaded.generate(**__lowerCamelCase							)
										self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Tuple:
							a    =			"hf-internal-testing/tiny-random-t5"
							a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

							a    =			model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
										with self.assertRaises(__lowerCamelCase							):
													model.save_pretrained(__lowerCamelCase							)

										a    =			model.reverse_bettertransformer()
										model.save_pretrained(__lowerCamelCase							)












class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3