
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_   (			__lowerCamelCase   ):




	'''simple docstring'''


	@slow
	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,    'prajjwal1/bert-tiny'       )
		lowercase       =    BertTokenizer.from_pretrained('bert-base-uncased'       )

		lowercase       =    bertabert.config.encoder.vocab_size
		lowercase       =    tokenizer.sep_token_id
		lowercase       =    tokenizer.cls_token_id
		lowercase       =    128

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

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

		lowercase       =    4

		def _map_to_encoder_decoder_inputs(snake_case       ):
			# Tokenizer will automatically set [BOS] <text> [EOS]
			lowercase       =    tokenizer(batch['article']		,    padding='max_length'		,    truncation=snake_case		,    max_length=512       )
			lowercase       =    tokenizer(batch['highlights']		,    padding='max_length'		,    truncation=snake_case		,    max_length=128       )
			lowercase       =    inputs.input_ids
			lowercase       =    inputs.attention_mask

			lowercase       =    outputs.input_ids
			lowercase       =    outputs.input_ids.copy()
			lowercase       =    [
			    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
			]
			lowercase       =    outputs.attention_mask

			assert all(len(snake_case       ) == 512 for x in inputs.input_ids       )
			assert all(len(snake_case       ) == 128 for x in outputs.input_ids       )

			return batch

		def _compute_metrics(snake_case       ):
			lowercase       =    pred.label_ids
			lowercase       =    pred.predictions

			# all unnecessary tokens are removed
			lowercase       =    tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case       )
			lowercase       =    tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case       )

			lowercase       =    sum([int(pred_str[i] == label_str[i]       ) for i in range(len(snake_case       )       )]       ) / len(snake_case       )

			return {"accuracy": accuracy}

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

		lowercase       =    self.get_auto_remove_tmp_dir()

		lowercase       =    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
		lowercase       =    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()



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)

UpperCAmelCase	      = {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       """xlnet"""
	_UpperCamelCase     :     Optional[Any]							 =       ["""mems"""]
	_UpperCamelCase     :     Tuple							 =       {
	    """n_token""": """vocab_size""",  # Backward compatibility
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(       self		,    snake_case=3_2000		,    snake_case=1024		,    snake_case=24		,    snake_case=16		,    snake_case=4096		,    snake_case="gelu"		,    snake_case=True		,    snake_case="bi"		,    snake_case=0.02		,    snake_case=1E-12		,    snake_case=0.1		,    snake_case=512		,    snake_case=None		,    snake_case=True		,    snake_case=False		,    snake_case=False		,    snake_case=-1		,    snake_case=False		,    snake_case="last"		,    snake_case=True		,    snake_case="tanh"		,    snake_case=0.1		,    snake_case=5		,    snake_case=5		,    snake_case=5		,    snake_case=1		,    snake_case=2		,    **snake_case		,    ):
		lowercase       =    vocab_size
		lowercase       =    d_model
		lowercase       =    n_layer
		lowercase       =    n_head
		if d_model % n_head != 0:
			raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''       )
		if "d_head" in kwargs:
			if kwargs["d_head"] != d_model // n_head:
				raise ValueError(
				    F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''       )
		lowercase       =    d_model // n_head
		lowercase       =    ff_activation
		lowercase       =    d_inner
		lowercase       =    untie_r
		lowercase       =    attn_type

		lowercase       =    initializer_range
		lowercase       =    layer_norm_eps

		lowercase       =    dropout
		lowercase       =    mem_len
		lowercase       =    reuse_len
		lowercase       =    bi_data
		lowercase       =    clamp_len
		lowercase       =    same_length

		lowercase       =    summary_type
		lowercase       =    summary_use_proj
		lowercase       =    summary_activation
		lowercase       =    summary_last_dropout
		lowercase       =    start_n_top
		lowercase       =    end_n_top

		lowercase       =    bos_token_id
		lowercase       =    pad_token_id
		lowercase       =    eos_token_id

		if "use_cache" in kwargs:
			warnings.warn(
			    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
			    ' instead.'		,    snake_case		,    )
			lowercase       =    kwargs['use_cache']

		lowercase       =    use_mems_eval
		lowercase       =    use_mems_train
		super().__init__(pad_token_id=snake_case		,    bos_token_id=snake_case		,    eos_token_id=snake_case		,    **snake_case       )





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
		return -1





	@max_position_embeddings.setter
	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# Message copied from Transformer-XL documentation
		raise NotImplementedError(
		    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )

