
'''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 					lowerCamelCase							(			self    ):





								"""simple docstring"""






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

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

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

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

								_snake_case  		=						4

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

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

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

									return batch

								def _compute_metrics(lowerCAmelCase_    ):
									_snake_case  		=						pred.label_ids
									_snake_case  		=						pred.predictions

									# all unnecessary tokens are removed
									_snake_case  		=						tokenizer.batch_decode(snake_case__       ,  skip_special_tokens=snake_case__    )
									_snake_case  		=						tokenizer.batch_decode(snake_case__       ,  skip_special_tokens=snake_case__    )

									_snake_case  		=						sum([int(pred_str[i] == label_str[i]    ) for i in range(len(snake_case__    )    )]    ) / len(snake_case__    )

									return {"accuracy": accuracy}

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

								_snake_case  		=						self.get_auto_remove_tmp_dir()

								_snake_case  		=						SeqaSeqTrainingArguments(
								    output_dir=snake_case__       ,  per_device_train_batch_size=snake_case__       ,  per_device_eval_batch_size=snake_case__       ,  predict_with_generate=snake_case__       ,  evaluation_strategy='steps'       ,  do_train=snake_case__       ,  do_eval=snake_case__       ,  warmup_steps=0       ,  eval_steps=2       ,  logging_steps=2       ,  )

								# instantiate trainer
								_snake_case  		=						SeqaSeqTrainer(
								    model=snake_case__       ,  args=snake_case__       ,  compute_metrics=_compute_metrics       ,  train_dataset=snake_case__       ,  eval_dataset=snake_case__       ,  tokenizer=snake_case__       ,  )

								# start training
								trainer.train()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



