


from typing import TYPE_CHECKING

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


lowerCamelCase		   =			{
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		   =			[
     'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LongformerForMaskedLM',
     'LongformerForMultipleChoice',
     'LongformerForQuestionAnswering',
     'LongformerForSequenceClassification',
     'LongformerForTokenClassification',
     'LongformerModel',
     'LongformerPreTrainedModel',
     'LongformerSelfAttention',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		   =			[
     'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLongformerForMaskedLM',
     'TFLongformerForMultipleChoice',
     'TFLongformerForQuestionAnswering',
     'TFLongformerForSequenceClassification',
     'TFLongformerForTokenClassification',
     'TFLongformerModel',
     'TFLongformerPreTrainedModel',
     'TFLongformerSelfAttention',
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

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






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 A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

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

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

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

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

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

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


