

"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]





def  _snake_case     (    UpperCAmelCase_  :					dict[int, list[int]]			):
     A__										=  0
     A__										=  len(UpperCAmelCase_			)  # No of vertices in graph
     A__										=  [0] * n
     A__										=  [False] * n

     def dfs(UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Optional[int]			):
          A__										=  True
          A__										=  id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   id_			)
                    A__										=  min(low[at]					,   low[to]			)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)			)
               else:
                    # This edge is a back edge and cannot be a bridge
                    A__										=  min(low[at]					,   low[to]			)

     A__										=  []
     for i in range(UpperCAmelCase_			):
          if not visited[i]:
               dfs(UpperCAmelCase_					,   -1					,   UpperCAmelCase_					,   id_			)
     return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






