
"""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     _UpperCAmelCase(    lowerCamelCase	):



		@slow
		@require_torch
		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''				,		'''prajjwal1/bert-tiny''')
							_UpperCamelCase							 =  BertTokenizer.from_pretrained('''bert-base-uncased''')

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

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

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

							_UpperCamelCase							 =  4

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

												_UpperCamelCase							 =  outputs.input_ids
												_UpperCamelCase							 =  outputs.input_ids.copy()
												_UpperCamelCase							 =  [
												    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												_UpperCamelCase							 =  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):
												_UpperCamelCase							 =  pred.label_ids
												_UpperCamelCase							 =  pred.predictions

												# all unnecessary tokens are removed
												_UpperCamelCase							 =  tokenizer.batch_decode(__a				,		skip_special_tokens=__a)
												_UpperCamelCase							 =  tokenizer.batch_decode(__a				,		skip_special_tokens=__a)

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

												return {"accuracy": accuracy}

							# map train dataset
							_UpperCamelCase							 =  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							 =  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							 =  self.get_auto_remove_tmp_dir()

							_UpperCamelCase							 =  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							 =  SeqaSeqTrainer(
							    model=__a				,		args=__a				,		compute_metrics=_compute_metrics				,		train_dataset=__a				,		eval_dataset=__a				,		tokenizer=__a				,		)

							# start training
							trainer.train()








"""simple docstring"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






