


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   ):




      '''simple docstring'''


      @slow
      @require_torch
      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,				'prajjwal1/bert-tiny'    )
       __lowerCamelCase		:							List[str]		 =						BertTokenizer.from_pretrained('bert-base-uncased'    )

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

       __lowerCamelCase		:							Dict		 =						datasets.load_dataset('cnn_dailymail'						,				'3.0.0'						,				split='train[:1%]'    )
       __lowerCamelCase		:							str		 =						datasets.load_dataset('cnn_dailymail'						,				'3.0.0'						,				split='validation[:1%]'    )

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

       __lowerCamelCase		:							Union[str, Any]		 =						4

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

        __lowerCamelCase		:							Optional[int]		 =						outputs.input_ids
        __lowerCamelCase		:							Optional[int]		 =						outputs.input_ids.copy()
        __lowerCamelCase		:							List[Any]		 =						[
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
        ]
        __lowerCamelCase		:							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(a:	List[Any]    ):
        __lowerCamelCase		:							int		 =						pred.label_ids
        __lowerCamelCase		:							Optional[Any]		 =						pred.predictions

        # all unnecessary tokens are removed
        __lowerCamelCase		:							List[str]		 =						tokenizer.batch_decode(a						,				skip_special_tokens=a    )
        __lowerCamelCase		:							List[str]		 =						tokenizer.batch_decode(a						,				skip_special_tokens=a    )

        __lowerCamelCase		:							str		 =						sum([int(pred_str[i] == label_str[i]    ) for i in range(len(a    )    )]    ) / len(a    )

        return {"accuracy": accuracy}

       # map train dataset
       __lowerCamelCase		:							Optional[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
       __lowerCamelCase		:							Optional[Any]		 =						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']						,				)

       __lowerCamelCase		:							Optional[int]		 =						self.get_auto_remove_tmp_dir()

       __lowerCamelCase		:							int		 =						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
       __lowerCamelCase		:							Tuple		 =						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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_	   =     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MBartForCausalLM',
           'MBartForConditionalGeneration',
           'MBartForQuestionAnswering',
           'MBartForSequenceClassification',
           'MBartModel',
           'MBartPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFMBartForConditionalGeneration',
           'TFMBartModel',
           'TFMBartPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'FlaxMBartForConditionalGeneration',
           'FlaxMBartForQuestionAnswering',
           'FlaxMBartForSequenceClassification',
           'FlaxMBartModel',
           'FlaxMBartPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

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

