

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				(				__A ):
			'''simple docstring'''







			@slow
			@require_torch
			def 				__UpperCamelCase			(      self				:			Any						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"   ,   "prajjwal1/bert-tiny"						)
				SCREAMING_SNAKE_CASE :   str					  =							BertTokenizer.from_pretrained("bert-base-uncased"						)

				SCREAMING_SNAKE_CASE :   int					  =							bertabert.config.encoder.vocab_size
				SCREAMING_SNAKE_CASE :   Dict					  =							tokenizer.sep_token_id
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tokenizer.cls_token_id
				SCREAMING_SNAKE_CASE :   List[Any]					  =							128

				SCREAMING_SNAKE_CASE :   str					  =							datasets.load_dataset("cnn_dailymail"   ,   "3.0.0"   ,   split="train[:1%]"						)
				SCREAMING_SNAKE_CASE :   str					  =							datasets.load_dataset("cnn_dailymail"   ,   "3.0.0"   ,   split="validation[:1%]"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							train_dataset.select(range(32						)						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							val_dataset.select(range(16						)						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							4

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

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

					# all unnecessary tokens are removed
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							tokenizer.batch_decode(a   ,   skip_special_tokens=a						)
					SCREAMING_SNAKE_CASE :   str					  =							tokenizer.batch_decode(a   ,   skip_special_tokens=a						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							sum([int(pred_str[i] == label_str[i]						) for i in range(len(a						)						)]						) / len(a						)

					return {"accuracy": accuracy}

				# map train dataset
				SCREAMING_SNAKE_CASE :   Dict					  =							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
				SCREAMING_SNAKE_CASE :   Union[str, 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"]   ,   )

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_auto_remove_tmp_dir()

				SCREAMING_SNAKE_CASE :   List[str]					  =							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
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)