






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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@slow
							@require_torch
							def       __A (  self:   Union[str, Any]   )    ->				Tuple:
												_A								= EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,     '''prajjwal1/bert-tiny'''   )
												_A								= BertTokenizer.from_pretrained('''bert-base-uncased'''   )

												_A								= bertabert.config.encoder.vocab_size
												_A								= tokenizer.sep_token_id
												_A								= tokenizer.cls_token_id
												_A								= 1_28

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

												_A								= train_dataset.select(range(32   )   )
												_A								= val_dataset.select(range(16   )   )

												_A								= 4

												def _map_to_encoder_decoder_inputs(__A:   int   ):
																	# Tokenizer will automatically set [BOS] <text> [EOS]
																	_A								= tokenizer(batch['''article''']		,     padding='''max_length'''		,     truncation=__A		,     max_length=5_12   )
																	_A								= tokenizer(batch['''highlights''']		,     padding='''max_length'''		,     truncation=__A		,     max_length=1_28   )
																	_A								= inputs.input_ids
																	_A								= inputs.attention_mask

																	_A								= outputs.input_ids
																	_A								= outputs.input_ids.copy()
																	_A								= [
																	    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																	]
																	_A								= outputs.attention_mask

																	assert all(len(__A   ) == 5_12 for x in inputs.input_ids   )
																	assert all(len(__A   ) == 1_28 for x in outputs.input_ids   )

																	return batch

												def _compute_metrics(__A:   int   ):
																	_A								= pred.label_ids
																	_A								= pred.predictions

																	# all unnecessary tokens are removed
																	_A								= tokenizer.batch_decode(__A		,     skip_special_tokens=__A   )
																	_A								= tokenizer.batch_decode(__A		,     skip_special_tokens=__A   )

																	_A								= sum([int(pred_str[i] == label_str[i]   ) for i in range(len(__A   )   )]   ) / len(__A   )

																	return {"accuracy": accuracy}

												# map train dataset
												_A								= 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
												_A								= 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''']		,     )

												_A								= self.get_auto_remove_tmp_dir()

												_A								= 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
												_A								= 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_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

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



