'''simple docstring'''





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  __magic_name__  (		_UpperCAmelCase    ):





   """simple docstring"""




   @slow
   @require_torch
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



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

    _UpperCamelCase:     Any							= bertabert.config.encoder.vocab_size
    _UpperCamelCase:     Tuple							= tokenizer.sep_token_id
    _UpperCamelCase:     Optional[int]							= tokenizer.cls_token_id
    _UpperCamelCase:     List[Any]							= 128

    _UpperCamelCase:     Tuple							= datasets.load_dataset('''cnn_dailymail'''		,  '''3.0.0'''		,  split='''train[:1%]'''							)
    _UpperCamelCase:     Optional[Any]							= datasets.load_dataset('''cnn_dailymail'''		,  '''3.0.0'''		,  split='''validation[:1%]'''							)

    _UpperCamelCase:     Optional[Any]							= train_dataset.select(range(32							)							)
    _UpperCamelCase:     Dict							= val_dataset.select(range(16							)							)

    _UpperCamelCase:     List[str]							= 4

    def _map_to_encoder_decoder_inputs(_lowercase					:  Optional[int]							):
     # Tokenizer will automatically set [BOS] <text> [EOS]
     _UpperCamelCase:     Union[str, Any]							= tokenizer(batch['''article''']		,  padding='''max_length'''		,  truncation=A_		,  max_length=512							)
     _UpperCamelCase:     Optional[Any]							= tokenizer(batch['''highlights''']		,  padding='''max_length'''		,  truncation=A_		,  max_length=128							)
     _UpperCamelCase:     Optional[int]							= inputs.input_ids
     _UpperCamelCase:     str							= inputs.attention_mask

     _UpperCamelCase:     List[str]							= outputs.input_ids
     _UpperCamelCase:     str							= outputs.input_ids.copy()
     _UpperCamelCase:     str							= [
         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
     ]
     _UpperCamelCase:     List[str]							= outputs.attention_mask

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

     return batch

    def _compute_metrics(_lowercase					:  str							):
     _UpperCamelCase:     Union[str, Any]							= pred.label_ids
     _UpperCamelCase:     int							= pred.predictions

     # all unnecessary tokens are removed
     _UpperCamelCase:     Dict							= tokenizer.batch_decode(A_		,  skip_special_tokens=A_							)
     _UpperCamelCase:     Any							= tokenizer.batch_decode(A_		,  skip_special_tokens=A_							)

     _UpperCamelCase:     int							= sum([int(pred_str[i] == label_str[i]							) for i in range(len(A_							)							)]							) / len(A_							)

     return {"accuracy": accuracy}

    # map train dataset
    _UpperCamelCase:     int							= train_dataset.map(
        _map_to_encoder_decoder_inputs		,  batched=A_		,  batch_size=A_		,  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
    _UpperCamelCase:     int							= val_dataset.map(
        _map_to_encoder_decoder_inputs		,  batched=A_		,  batch_size=A_		,  remove_columns=['''article''', '''highlights''']		,  )
    val_dataset.set_format(
        type='''torch'''		,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,  )

    _UpperCamelCase:     Any							= self.get_auto_remove_tmp_dir()

    _UpperCamelCase:     Any							= SeqaSeqTrainingArguments(
        output_dir=A_		,  per_device_train_batch_size=A_		,  per_device_eval_batch_size=A_		,  predict_with_generate=A_		,  evaluation_strategy='''steps'''		,  do_train=A_		,  do_eval=A_		,  warmup_steps=0		,  eval_steps=2		,  logging_steps=2		,  )

    # instantiate trainer
    _UpperCamelCase:     Any							= SeqaSeqTrainer(
        model=A_		,  args=A_		,  compute_metrics=_compute_metrics		,  train_dataset=A_		,  eval_dataset=A_		,  tokenizer=A_		,  )

    # start training
    trainer.train()
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)