




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       A   (		UpperCamelCase__   ):







							@slow
							@require_torch
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							List[str]:


														"""simple docstring"""


														_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(lowerCAmelCase_	:  List[Any] ):
																					# Tokenizer will automatically set [BOS] <text> [EOS]
																					_a     					=    tokenizer(batch['''article''']							,      padding='''max_length'''							,      truncation=__lowerCamelCase							,      max_length=5_12 )
																					_a     					=    tokenizer(batch['''highlights''']							,      padding='''max_length'''							,      truncation=__lowerCamelCase							,      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(__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_	:  Tuple ):
																					_a     					=    pred.label_ids
																					_a     					=    pred.predictions

																					# all unnecessary tokens are removed
																					_a     					=    tokenizer.batch_decode(__lowerCamelCase							,      skip_special_tokens=__lowerCamelCase )
																					_a     					=    tokenizer.batch_decode(__lowerCamelCase							,      skip_special_tokens=__lowerCamelCase )

																					_a     					=    sum([int(pred_str[i] == label_str[i] ) for i in range(len(__lowerCamelCase ) )] ) / len(__lowerCamelCase )

																					return {"accuracy": accuracy}

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

														_a     					=    self.get_auto_remove_tmp_dir()

														_a     					=    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
														_a     					=    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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				Dict       							=		{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		['AlbertTokenizer']

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AlbertForMaskedLM',
				    'AlbertForMultipleChoice',
				    'AlbertForPreTraining',
				    'AlbertForQuestionAnswering',
				    'AlbertForSequenceClassification',
				    'AlbertForTokenClassification',
				    'AlbertModel',
				    'AlbertPreTrainedModel',
				    'load_tf_weights_in_albert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Any       							=		[
				    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFAlbertForMaskedLM',
				    'TFAlbertForMultipleChoice',
				    'TFAlbertForPreTraining',
				    'TFAlbertForQuestionAnswering',
				    'TFAlbertForSequenceClassification',
				    'TFAlbertForTokenClassification',
				    'TFAlbertMainLayer',
				    'TFAlbertModel',
				    'TFAlbertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Any       							=		[
				    'FlaxAlbertForMaskedLM',
				    'FlaxAlbertForMultipleChoice',
				    'FlaxAlbertForPreTraining',
				    'FlaxAlbertForQuestionAnswering',
				    'FlaxAlbertForSequenceClassification',
				    'FlaxAlbertForTokenClassification',
				    'FlaxAlbertModel',
				    'FlaxAlbertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert import AlbertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert_fast import AlbertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_albert import (
								    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AlbertForMaskedLM,
								    AlbertForMultipleChoice,
								    AlbertForPreTraining,
								    AlbertForQuestionAnswering,
								    AlbertForSequenceClassification,
								    AlbertForTokenClassification,
								    AlbertModel,
								    AlbertPreTrainedModel,
								    load_tf_weights_in_albert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_albert import (
								    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFAlbertForMaskedLM,
								    TFAlbertForMultipleChoice,
								    TFAlbertForPreTraining,
								    TFAlbertForQuestionAnswering,
								    TFAlbertForSequenceClassification,
								    TFAlbertForTokenClassification,
								    TFAlbertMainLayer,
								    TFAlbertModel,
								    TFAlbertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_albert import (
								    FlaxAlbertForMaskedLM,
								    FlaxAlbertForMultipleChoice,
								    FlaxAlbertForPreTraining,
								    FlaxAlbertForQuestionAnswering,
								    FlaxAlbertForSequenceClassification,
								    FlaxAlbertForTokenClassification,
								    FlaxAlbertModel,
								    FlaxAlbertPreTrainedModel,
								)
else:
				import sys

				_snake_case				:				Union[str, Any]       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

