





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"""



   __A       :    List[str]	  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __A       :    Any	  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Any			=    TextaTextGenerationPipeline(model=lowercase ,  tokenizer=lowercase)
          return generator, ["Something to write", "Something else"]

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

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

          a__						:     Optional[int]			=    generator(['This is great !', 'Something else'] ,  num_return_sequences=2 ,  do_sample=lowercase)
          self.assertEqual(
              lowercase ,  [
                  [{'generated_text': ANY(lowercase)}, {'generated_text': ANY(lowercase)}],
                  [{'generated_text': ANY(lowercase)}, {'generated_text': ANY(lowercase)}],
              ] ,  )

          a__						:     Any			=    generator(
              ['This is great !', 'Something else'] ,  num_return_sequences=2 ,  batch_size=2 ,  do_sample=lowercase)
          self.assertEqual(
              lowercase ,  [
                  [{'generated_text': ANY(lowercase)}, {'generated_text': ANY(lowercase)}],
                  [{'generated_text': ANY(lowercase)}, {'generated_text': ANY(lowercase)}],
              ] ,  )

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

   @require_torch
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    pipeline('text2text-generation' ,  model='patrickvonplaten/t5-tiny-random' ,  framework='pt')
          # do_sample=False necessary for reproducibility
          a__						:     Union[str, Any]			=    generator('Something there' ,  do_sample=lowercase)
          self.assertEqual(lowercase ,  [{'generated_text': ''}])

          a__						:     List[str]			=    3
          a__						:     List[Any]			=    generator(
              'Something there' ,  num_return_sequences=lowercase ,  num_beams=lowercase ,  )
          a__						:     Union[str, Any]			=    [
              {'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(lowercase ,  lowercase)

          a__						:     int			=    generator('This is a test' ,  do_sample=lowercase ,  num_return_sequences=2 ,  return_tensors=lowercase)
          self.assertEqual(
              lowercase ,  [
                  {'generated_token_ids': ANY(torch.Tensor)},
                  {'generated_token_ids': ANY(torch.Tensor)},
              ] ,  )
          a__						:     Tuple			=    generator.model.config.eos_token_id
          a__						:     Any			=    '<pad>'
          a__						:     Tuple			=    generator(
              ['This is a test', 'This is a second test'] ,  do_sample=lowercase ,  num_return_sequences=2 ,  batch_size=2 ,  return_tensors=lowercase ,  )
          self.assertEqual(
              lowercase ,  [
                  [
                      {'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 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     Optional[Any]			=    pipeline('text2text-generation' ,  model='patrickvonplaten/t5-tiny-random' ,  framework='tf')
          # do_sample=False necessary for reproducibility
          a__						:     Optional[int]			=    generator('Something there' ,  do_sample=lowercase)
          self.assertEqual(lowercase ,  [{'generated_text': ''}])









def A_     (		A__    )  ->		List[str]:  # noqa: E741
       a__						:     Dict			=    len(A__    )
       a__						:     str			=    0
       a__						:     Any			=    [0] * n
       a__						:     int			=    [False] * n
       a__						:     Optional[Any]			=    [False] * n

       def dfs(A__		,				A__		,				A__		,				A__    ):
              if parent == root:
                     out_edge_count += 1
              a__						:     Union[str, Any]			=    True
              a__						:     Optional[Any]			=    at

              for to in l[at]:
                     if to == parent:
                            pass
                     elif not visited[to]:
                            a__						:     List[Any]			=    dfs(A__		,				A__		,				A__		,				A__    )
                            a__						:     Dict			=    min(low[at]		,				low[to]    )

                            # AP found via bridge
                            if at < low[to]:
                                   a__						:     Dict			=    True
                            # AP found via cycle
                            if at == low[to]:
                                   a__						:     List[Any]			=    True
                     else:
                            a__						:     Optional[int]			=    min(low[at]		,				A__    )
              return out_edge_count

       for i in range(A__    ):
              if not visited[i]:
                     a__						:     Tuple			=    0
                     a__						:     Any			=    dfs(A__		,				A__		,				-1		,				A__    )
                     a__						:     List[Any]			=    out_edge_count > 1

       for x in range(len(A__    )    ):
              if is_art[x] is True:
                     print(A__    )


# Adjacency list of graph
lowercase				:	List[Any]      						=  {
    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],
}



compute_ap(data)


