



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
  import datasets







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   @slow
   @require_torch
   def      UpperCamelCase_  (					self							:			List[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  Any					=					EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,		"""prajjwal1/bert-tiny""")
    _snake_case    :  Union[str, Any]					=					BertTokenizer.from_pretrained("""bert-base-uncased""")

    _snake_case    :  Tuple					=					bertabert.config.encoder.vocab_size
    _snake_case    :  List[str]					=					tokenizer.sep_token_id
    _snake_case    :  Tuple					=					tokenizer.cls_token_id
    _snake_case    :  Dict					=					128

    _snake_case    :  List[Any]					=					datasets.load_dataset("""cnn_dailymail"""				,		"""3.0.0"""				,		split="""train[:1%]""")
    _snake_case    :  List[Any]					=					datasets.load_dataset("""cnn_dailymail"""				,		"""3.0.0"""				,		split="""validation[:1%]""")

    _snake_case    :  Tuple					=					train_dataset.select(range(32))
    _snake_case    :  int					=					val_dataset.select(range(16))

    _snake_case    :  List[Any]					=					4

    def _map_to_encoder_decoder_inputs(lowerCAmelCase							:			List[Any]):
     # Tokenizer will automatically set [BOS] <text> [EOS]
     _snake_case    :  Optional[Any]					=					tokenizer(batch["""article"""]				,		padding="""max_length"""				,		truncation=lowerCAmelCase				,		max_length=512)
     _snake_case    :  List[Any]					=					tokenizer(batch["""highlights"""]				,		padding="""max_length"""				,		truncation=lowerCAmelCase				,		max_length=128)
     _snake_case    :  Any					=					inputs.input_ids
     _snake_case    :  Dict					=					inputs.attention_mask

     _snake_case    :  List[Any]					=					outputs.input_ids
     _snake_case    :  int					=					outputs.input_ids.copy()
     _snake_case    :  Optional[int]					=					[
         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
     ]
     _snake_case    :  Optional[Any]					=					outputs.attention_mask

     assert all(len(lowerCAmelCase) == 512 for x in inputs.input_ids)
     assert all(len(lowerCAmelCase) == 128 for x in outputs.input_ids)

     return batch

    def _compute_metrics(lowerCAmelCase							:			Any):
     _snake_case    :  Tuple					=					pred.label_ids
     _snake_case    :  Any					=					pred.predictions

     # all unnecessary tokens are removed
     _snake_case    :  int					=					tokenizer.batch_decode(lowerCAmelCase				,		skip_special_tokens=lowerCAmelCase)
     _snake_case    :  Any					=					tokenizer.batch_decode(lowerCAmelCase				,		skip_special_tokens=lowerCAmelCase)

     _snake_case    :  Any					=					sum([int(pred_str[i] == label_str[i]) for i in range(len(lowerCAmelCase))]) / len(lowerCAmelCase)

     return {"accuracy": accuracy}

    # map train dataset
    _snake_case    :  Tuple					=					train_dataset.map(
        _map_to_encoder_decoder_inputs				,		batched=lowerCAmelCase				,		batch_size=lowerCAmelCase				,		remove_columns=["""article""", """highlights"""]				,		)
    train_dataset.set_format(
        type="""torch"""				,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,		)

    # same for validation dataset
    _snake_case    :  Union[str, Any]					=					val_dataset.map(
        _map_to_encoder_decoder_inputs				,		batched=lowerCAmelCase				,		batch_size=lowerCAmelCase				,		remove_columns=["""article""", """highlights"""]				,		)
    val_dataset.set_format(
        type="""torch"""				,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,		)

    _snake_case    :  List[Any]					=					self.get_auto_remove_tmp_dir()

    _snake_case    :  Optional[int]					=					SeqaSeqTrainingArguments(
        output_dir=lowerCAmelCase				,		per_device_train_batch_size=lowerCAmelCase				,		per_device_eval_batch_size=lowerCAmelCase				,		predict_with_generate=lowerCAmelCase				,		evaluation_strategy="""steps"""				,		do_train=lowerCAmelCase				,		do_eval=lowerCAmelCase				,		warmup_steps=0				,		eval_steps=2				,		logging_steps=2				,		)

    # instantiate trainer
    _snake_case    :  int					=					SeqaSeqTrainer(
        model=lowerCAmelCase				,		args=lowerCAmelCase				,		compute_metrics=_compute_metrics				,		train_dataset=lowerCAmelCase				,		eval_dataset=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		)

    # start training
    trainer.train()





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
