






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					@slow
					@require_torch
					def  _lowercase  (      self  :						Any ):
						snake_case__				:   List[str]     =						EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,	"prajjwal1/bert-tiny" )
						snake_case__				:   Optional[Any]     =						BertTokenizer.from_pretrained("bert-base-uncased" )

						snake_case__				:   Any     =						bertabert.config.encoder.vocab_size
						snake_case__				:   List[str]     =						tokenizer.sep_token_id
						snake_case__				:   Any     =						tokenizer.cls_token_id
						snake_case__				:   Tuple     =						1_2_8

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

						snake_case__				:   Optional[int]     =						train_dataset.select(range(3_2 ) )
						snake_case__				:   Tuple     =						val_dataset.select(range(1_6 ) )

						snake_case__				:   List[str]     =						4

						def _map_to_encoder_decoder_inputs(__A  :						Optional[int] ):
							# Tokenizer will automatically set [BOS] <text> [EOS]
							snake_case__				:   Dict     =						tokenizer(batch["article"]							,	padding="max_length"							,	truncation=__A							,	max_length=5_1_2 )
							snake_case__				:   List[str]     =						tokenizer(batch["highlights"]							,	padding="max_length"							,	truncation=__A							,	max_length=1_2_8 )
							snake_case__				:   str     =						inputs.input_ids
							snake_case__				:   int     =						inputs.attention_mask

							snake_case__				:   Tuple     =						outputs.input_ids
							snake_case__				:   Dict     =						outputs.input_ids.copy()
							snake_case__				:   Optional[Any]     =						[
							    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
							]
							snake_case__				:   List[Any]     =						outputs.attention_mask

							assert all(len(__A ) == 5_1_2 for x in inputs.input_ids )
							assert all(len(__A ) == 1_2_8 for x in outputs.input_ids )

							return batch

						def _compute_metrics(__A  :						Union[str, Any] ):
							snake_case__				:   Dict     =						pred.label_ids
							snake_case__				:   Any     =						pred.predictions

							# all unnecessary tokens are removed
							snake_case__				:   str     =						tokenizer.batch_decode(__A							,	skip_special_tokens=__A )
							snake_case__				:   Any     =						tokenizer.batch_decode(__A							,	skip_special_tokens=__A )

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

							return {"accuracy": accuracy}

						# map train dataset
						snake_case__				:   Tuple     =						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
						snake_case__				:   int     =						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"]							,	)

						snake_case__				:   Dict     =						self.get_auto_remove_tmp_dir()

						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
						snake_case__				:   Dict     =						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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



