



'''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 	lowerCamelCase_							(					snake_case_			):







						@slow
						@require_torch
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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					:      Union[str, Any]     					=			tokenizer.cls_token_id
													SCREAMING_SNAKE_CASE					:      Any     					=			1_28

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

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

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			4

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

																				SCREAMING_SNAKE_CASE					:      List[str]     					=			outputs.input_ids
																				SCREAMING_SNAKE_CASE					:      int     					=			outputs.input_ids.copy()
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			[
																				    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																				]
																				SCREAMING_SNAKE_CASE					:      Any     					=			outputs.attention_mask

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

																				return batch

													def _compute_metrics(lowerCAmelCase__   :      Any				):
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			pred.label_ids
																				SCREAMING_SNAKE_CASE					:      str     					=			pred.predictions

																				# all unnecessary tokens are removed
																				SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.batch_decode(lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.batch_decode(lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)

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

																				return {"accuracy": accuracy}

													# map train dataset
													SCREAMING_SNAKE_CASE					:      Tuple     					=			train_dataset.map(
													    _map_to_encoder_decoder_inputs				,    batched=lowerCAmelCase__				,    batch_size=lowerCAmelCase__				,    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					:      List[str]     					=			val_dataset.map(
													    _map_to_encoder_decoder_inputs				,    batched=lowerCAmelCase__				,    batch_size=lowerCAmelCase__				,    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					:      int     					=			self.get_auto_remove_tmp_dir()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			SeqaSeqTrainingArguments(
													    output_dir=lowerCAmelCase__				,    per_device_train_batch_size=lowerCAmelCase__				,    per_device_eval_batch_size=lowerCAmelCase__				,    predict_with_generate=lowerCAmelCase__				,    evaluation_strategy='''steps'''				,    do_train=lowerCAmelCase__				,    do_eval=lowerCAmelCase__				,    warmup_steps=0				,    eval_steps=2				,    logging_steps=2				,    )

													# instantiate trainer
													SCREAMING_SNAKE_CASE					:      int     					=			SeqaSeqTrainer(
													    model=lowerCAmelCase__				,    args=lowerCAmelCase__				,    compute_metrics=_compute_metrics				,    train_dataset=lowerCAmelCase__				,    eval_dataset=lowerCAmelCase__				,    tokenizer=lowerCAmelCase__				,    )

													# start training
													trainer.train()









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     :       str								=						{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Optional[int]								=						[
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							lowerCAmelCase_     :       Optional[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




