



import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


SCREAMING_SNAKE_CASE     				=						logging.getLogger(__name__)



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  'summarization'
					lowerCAmelCase_				:    Any									=  ['loss']
					lowerCAmelCase_				:    List[str]									=  ROUGE_KEYS
					lowerCAmelCase_				:    Dict									=  'rouge2'



					def __init__(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							if hparams.sortish_sampler and hparams.gpus > 1:
									UpperCAmelCase_						=				False
							elif hparams.max_tokens_per_batch is not None:
									if hparams.gpus > 1:
											raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"     )
									if hparams.sortish_sampler:
											raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"     )

							super().__init__(lowerCAmelCase    ,	num_labels=lowerCAmelCase    ,	mode=self.mode    ,	**lowerCAmelCase     )
							use_task_specific_params(self.model    ,	"summarization"     )
							save_git_info(self.hparams.output_dir     )
							UpperCAmelCase_						=				Path(self.output_dir     ) / "metrics.json"
							UpperCAmelCase_						=				Path(self.output_dir     ) / "hparams.pkl"
							pickle_save(self.hparams    ,	self.hparams_save_path     )
							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				defaultdict(lowerCAmelCase     )
							UpperCAmelCase_						=				self.config.model_type
							UpperCAmelCase_						=				self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

							UpperCAmelCase_						=				{
							    "data_dir": self.hparams.data_dir,
							    "max_source_length": self.hparams.max_source_length,
							    "prefix": self.model.config.prefix or "",
							}
							UpperCAmelCase_						=				{
							    "train": self.hparams.n_train,
							    "val": self.hparams.n_val,
							    "test": self.hparams.n_test,
							}
							UpperCAmelCase_						=				{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

							UpperCAmelCase_						=				{
							    "train": self.hparams.max_target_length,
							    "val": self.hparams.val_max_target_length,
							    "test": self.hparams.test_max_target_length,
							}
							assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
							assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
							if self.hparams.freeze_embeds:
									freeze_embeds(self.model     )
							if self.hparams.freeze_encoder:
									freeze_params(self.model.get_encoder()     )
									assert_all_frozen(self.model.get_encoder()     )

							UpperCAmelCase_						=				get_git_info()["repo_sha"]
							UpperCAmelCase_						=				hparams.num_workers
							UpperCAmelCase_						=				None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,	lowerCAmelCase     ):
									UpperCAmelCase_						=				self.tokenizer.lang_code_to_id[hparams.tgt_lang]
									UpperCAmelCase_						=				self.decoder_start_token_id
							UpperCAmelCase_						=				(
							    SeqaSeqDataset if hasattr(self.tokenizer    ,	"prepare_seq2seq_batch"     ) else LegacySeqaSeqDataset
							)
							UpperCAmelCase_						=				False
							UpperCAmelCase_						=				self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
							if self.hparams.eval_max_gen_length is not None:
									UpperCAmelCase_						=				self.hparams.eval_max_gen_length
							else:
									UpperCAmelCase_						=				self.model.config.max_length
							UpperCAmelCase_						=				self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				{
							    k: self.tokenizer.batch_decode(v.tolist()     ) if "mask" not in k else v.shape for k, v in batch.items()
							}
							save_json(lowerCAmelCase    ,	Path(self.output_dir     ) / "text_batch.json"     )
							save_json({k: v.tolist() for k, v in batch.items()}    ,	Path(self.output_dir     ) / "tok_batch.json"     )

							UpperCAmelCase_						=				True
							return readable_batch



					def    A__		(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							return self.model(lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.tokenizer.batch_decode(
							    lowerCAmelCase    ,	skip_special_tokens=lowerCAmelCase    ,	clean_up_tokenization_spaces=lowerCAmelCase     )
							return lmap(str.strip    ,	lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.tokenizer.pad_token_id
							UpperCAmelCase_					,						UpperCAmelCase_						=				batch["input_ids"], batch["attention_mask"]
							UpperCAmelCase_						=				batch["labels"]
							if isinstance(self.model    ,	lowerCAmelCase     ):
									UpperCAmelCase_						=				self.model._shift_right(lowerCAmelCase     )
							else:
									UpperCAmelCase_						=				shift_tokens_right(lowerCAmelCase    ,	lowerCAmelCase     )
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
									UpperCAmelCase_						=				decoder_input_ids
									self.save_readable_batch(lowerCAmelCase     )

							UpperCAmelCase_						=				self(lowerCAmelCase    ,	attention_mask=lowerCAmelCase    ,	decoder_input_ids=lowerCAmelCase    ,	use_cache=lowerCAmelCase     )
							UpperCAmelCase_						=				outputs["logits"]
							if self.hparams.label_smoothing == 0:
									# Same behavior as modeling_bart.py, besides ignoring pad_token_id
									UpperCAmelCase_						=				nn.CrossEntropyLoss(ignore_index=lowerCAmelCase     )

									assert lm_logits.shape[-1] == self.vocab_size
									UpperCAmelCase_						=				ce_loss_fct(lm_logits.view(-1    ,	lm_logits.shape[-1]     )    ,	tgt_ids.view(-1     )     )
							else:
									UpperCAmelCase_						=				nn.functional.log_softmax(lowerCAmelCase    ,	dim=-1     )
									UpperCAmelCase_					,						UpperCAmelCase_						=				label_smoothed_nll_loss(
									    lowerCAmelCase    ,	lowerCAmelCase    ,	self.hparams.label_smoothing    ,	ignore_index=lowerCAmelCase     )
							return (loss,)



					@property
					def    A__		(			self     ):
							return self.tokenizer.pad_token_id



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self._step(lowerCAmelCase     )

							UpperCAmelCase_						=				dict(zip(self.loss_names    ,	lowerCAmelCase     )     )
							# tokens per batch
							UpperCAmelCase_						=				batch["input_ids"].ne(self.pad     ).sum() + batch["labels"].ne(self.pad     ).sum()
							UpperCAmelCase_						=				batch["input_ids"].shape[0]
							UpperCAmelCase_						=				batch["input_ids"].eq(self.pad     ).sum()
							UpperCAmelCase_						=				batch["input_ids"].eq(self.pad     ).float().mean()
							# TODO(SS): make a wandb summary metric for this
							return {"loss": loss_tensors[0], "log": logs}



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							return self._generative_step(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase="val"     ):
							self.step_count += 1
							UpperCAmelCase_						=				{k: torch.stack([x[k] for x in outputs]     ).mean() for k in self.loss_names}
							UpperCAmelCase_						=				losses["loss"]
							UpperCAmelCase_						=				{
							    k: np.array([x[k] for x in outputs]     ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
							}
							UpperCAmelCase_						=				(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							UpperCAmelCase_						=				torch.tensor(lowerCAmelCase     ).type_as(lowerCAmelCase     )
							generative_metrics.update({k: v.item() for k, v in losses.items()}     )
							losses.update(lowerCAmelCase     )
							UpperCAmelCase_						=				{f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
							UpperCAmelCase_						=				self.step_count
							self.metrics[prefix].append(lowerCAmelCase     )  # callback writes this to self.metrics_save_path
							UpperCAmelCase_						=				flatten_list([x["preds"] for x in outputs]     )
							return {
							    "log": all_metrics,
							    "preds": preds,
							    f'''{prefix}_loss''': loss,
							    f'''{prefix}_{self.val_metric}''': metric_tensor,
							}



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							return calculate_rouge(lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				time.time()

							# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
							UpperCAmelCase_						=				self.model.generate(
							    batch["input_ids"]    ,	attention_mask=batch["attention_mask"]    ,	use_cache=lowerCAmelCase    ,	decoder_start_token_id=self.decoder_start_token_id    ,	num_beams=self.eval_beams    ,	max_length=self.eval_max_length    ,	)
							UpperCAmelCase_						=				(time.time() - ta) / batch["input_ids"].shape[0]
							UpperCAmelCase_						=				self.ids_to_clean_text(lowerCAmelCase     )
							UpperCAmelCase_						=				self.ids_to_clean_text(batch["labels"]     )
							UpperCAmelCase_						=				self._step(lowerCAmelCase     )
							UpperCAmelCase_						=				dict(zip(self.loss_names    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				self.calc_generative_metrics(lowerCAmelCase    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				np.mean(lmap(lowerCAmelCase    ,	lowerCAmelCase     )     )
							base_metrics.update(gen_time=lowerCAmelCase    ,	gen_len=lowerCAmelCase    ,	preds=lowerCAmelCase    ,	target=lowerCAmelCase    ,	**lowerCAmelCase     )
							return base_metrics



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							return self._generative_step(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return self.validation_epoch_end(lowerCAmelCase    ,	prefix="test"     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.n_obs[type_path]
							UpperCAmelCase_						=				self.target_lens[type_path]
							UpperCAmelCase_						=				self.dataset_class(
							    self.tokenizer    ,	type_path=lowerCAmelCase    ,	n_obs=lowerCAmelCase    ,	max_target_length=lowerCAmelCase    ,	**self.dataset_kwargs    ,	)
							return dataset



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False     ):
							UpperCAmelCase_						=				self.get_dataset(lowerCAmelCase     )

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
									UpperCAmelCase_						=				dataset.make_sortish_sampler(lowerCAmelCase    ,	distributed=self.hparams.gpus > 1     )
									return DataLoader(
									    lowerCAmelCase    ,	batch_size=lowerCAmelCase    ,	collate_fn=dataset.collate_fn    ,	shuffle=lowerCAmelCase    ,	num_workers=self.num_workers    ,	sampler=lowerCAmelCase    ,	)

							elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
									UpperCAmelCase_						=				dataset.make_dynamic_sampler(
									    self.hparams.max_tokens_per_batch    ,	distributed=self.hparams.gpus > 1     )
									return DataLoader(
									    lowerCAmelCase    ,	batch_sampler=lowerCAmelCase    ,	collate_fn=dataset.collate_fn    ,	num_workers=self.num_workers    ,	)
							else:
									return DataLoader(
									    lowerCAmelCase    ,	batch_size=lowerCAmelCase    ,	collate_fn=dataset.collate_fn    ,	shuffle=lowerCAmelCase    ,	num_workers=self.num_workers    ,	sampler=lowerCAmelCase    ,	)



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.get_dataloader("train"    ,	batch_size=self.hparams.train_batch_size    ,	shuffle=lowerCAmelCase     )
							return dataloader



					def    A__		(			self     ):
							return self.get_dataloader("val"    ,	batch_size=self.hparams.eval_batch_size     )



					def    A__		(			self     ):
							return self.get_dataloader("test"    ,	batch_size=self.hparams.eval_batch_size     )



					@staticmethod
					def    A__		(			lowerCAmelCase    ,	lowerCAmelCase     ):
							BaseTransformer.add_model_specific_args(lowerCAmelCase    ,	lowerCAmelCase     )
							add_generic_args(lowerCAmelCase    ,	lowerCAmelCase     )
							parser.add_argument(
							    "--max_source_length"    ,	default=1024    ,	type=lowerCAmelCase    ,	help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    )    ,	)
							parser.add_argument(
							    "--max_target_length"    ,	default=56    ,	type=lowerCAmelCase    ,	help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    )    ,	)
							parser.add_argument(
							    "--val_max_target_length"    ,	default=142    ,	type=lowerCAmelCase    ,	help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    )    ,	)
							parser.add_argument(
							    "--test_max_target_length"    ,	default=142    ,	type=lowerCAmelCase    ,	help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    )    ,	)
							parser.add_argument("--freeze_encoder"    ,	action="store_true"     )
							parser.add_argument("--freeze_embeds"    ,	action="store_true"     )
							parser.add_argument("--sortish_sampler"    ,	action="store_true"    ,	default=lowerCAmelCase     )
							parser.add_argument("--overwrite_output_dir"    ,	action="store_true"    ,	default=lowerCAmelCase     )
							parser.add_argument("--max_tokens_per_batch"    ,	type=lowerCAmelCase    ,	default=lowerCAmelCase     )
							parser.add_argument("--logger_name"    ,	type=lowerCAmelCase    ,	choices=["default", "wandb", "wandb_shared"]    ,	default="default"     )
							parser.add_argument("--n_train"    ,	type=lowerCAmelCase    ,	default=-1    ,	required=lowerCAmelCase    ,	help="# examples. -1 means use all."     )
							parser.add_argument("--n_val"    ,	type=lowerCAmelCase    ,	default=500    ,	required=lowerCAmelCase    ,	help="# examples. -1 means use all."     )
							parser.add_argument("--n_test"    ,	type=lowerCAmelCase    ,	default=-1    ,	required=lowerCAmelCase    ,	help="# examples. -1 means use all."     )
							parser.add_argument(
							    "--task"    ,	type=lowerCAmelCase    ,	default="summarization"    ,	required=lowerCAmelCase    ,	help="# examples. -1 means use all."     )
							parser.add_argument("--label_smoothing"    ,	type=lowerCAmelCase    ,	default=0.0    ,	required=lowerCAmelCase     )
							parser.add_argument("--src_lang"    ,	type=lowerCAmelCase    ,	default=""    ,	required=lowerCAmelCase     )
							parser.add_argument("--tgt_lang"    ,	type=lowerCAmelCase    ,	default=""    ,	required=lowerCAmelCase     )
							parser.add_argument("--eval_beams"    ,	type=lowerCAmelCase    ,	default=lowerCAmelCase    ,	required=lowerCAmelCase     )
							parser.add_argument(
							    "--val_metric"    ,	type=lowerCAmelCase    ,	default=lowerCAmelCase    ,	required=lowerCAmelCase    ,	choices=["bleu", "rouge2", "loss", None]     )
							parser.add_argument("--eval_max_gen_length"    ,	type=lowerCAmelCase    ,	default=lowerCAmelCase    ,	help="never generate more than n tokens"     )
							parser.add_argument("--save_top_k"    ,	type=lowerCAmelCase    ,	default=1    ,	required=lowerCAmelCase    ,	help="How many checkpoints to save"     )
							parser.add_argument(
							    "--early_stopping_patience"    ,	type=lowerCAmelCase    ,	default=-1    ,	required=lowerCAmelCase    ,	help=(
							        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
							        " val_check_interval will effect it."
							    )    ,	)
							return parser





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  'translation'
					lowerCAmelCase_				:    Optional[Any]									=  ['loss']
					lowerCAmelCase_				:    Optional[Any]									=  ['bleu']
					lowerCAmelCase_				:    List[str]									=  'bleu'



					def __init__(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							super().__init__(lowerCAmelCase    ,	**lowerCAmelCase     )
							UpperCAmelCase_						=				hparams.src_lang
							UpperCAmelCase_						=				hparams.tgt_lang



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							return calculate_bleu(lowerCAmelCase    ,	lowerCAmelCase     )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None )		->							SummarizationModule:
		Path(args.output_dir ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
		check_output_dir(__SCREAMING_SNAKE_CASE			,     expected_items=3 )

		if model is None:
				if "summarization" in args.task:
						UpperCAmelCase_						=				SummarizationModule(__SCREAMING_SNAKE_CASE )
				else:
						UpperCAmelCase_						=				TranslationModule(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				Path(args.data_dir ).name
		if (
		    args.logger_name == "default"
		    or args.fast_dev_run
		    or str(args.output_dir ).startswith("/tmp" )
		    or str(args.output_dir ).startswith("/var" )
		):
				UpperCAmelCase_						=				True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				UpperCAmelCase_						=				os.environ.get("WANDB_PROJECT"			,     __SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				WandbLogger(name=model.output_dir.name			,     project=__SCREAMING_SNAKE_CASE )

		elif args.logger_name == "wandb_shared":
				from pytorch_lightning.loggers import WandbLogger

				UpperCAmelCase_						=				WandbLogger(name=model.output_dir.name			,     project=f'''hf_{dataset}''' )

		if args.early_stopping_patience >= 0:
				UpperCAmelCase_						=				get_early_stopping_callback(model.val_metric			,     args.early_stopping_patience )
		else:
				UpperCAmelCase_						=				False

		UpperCAmelCase_						=				args.val_metric == "loss"
		UpperCAmelCase_						=				generic_train(
		    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     logging_callback=SeqaSeqLoggingCallback()			,     checkpoint_callback=get_checkpoint_callback(
		        args.output_dir			,     model.val_metric			,     args.save_top_k			,     __SCREAMING_SNAKE_CASE )			,     early_stopping_callback=__SCREAMING_SNAKE_CASE			,     logger=__SCREAMING_SNAKE_CASE			,     )
		pickle_save(model.hparams			,     model.output_dir / "hparams.pkl" )
		if not args.do_predict:
				return model

		UpperCAmelCase_						=				""
		UpperCAmelCase_						=				sorted(glob.glob(os.path.join(args.output_dir			,     "*.ckpt" )			,     recursive=__SCREAMING_SNAKE_CASE ) )
		if checkpoints:
				UpperCAmelCase_						=				checkpoints[-1]
				UpperCAmelCase_						=				checkpoints[-1]
		trainer.logger.log_hyperparams(model.hparams )

		# test() without a model tests using the best checkpoint automatically
		trainer.test()
		return model


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						argparse.ArgumentParser()
			SCREAMING_SNAKE_CASE     				=						pl.Trainer.add_argparse_args(parser)
			SCREAMING_SNAKE_CASE     				=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

			SCREAMING_SNAKE_CASE     				=						parser.parse_args()

			main(args)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  'xlm-roberta'



					def __init__(			self    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase="absolute"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(pad_token_id=lowerCAmelCase    ,	bos_token_id=lowerCAmelCase    ,	eos_token_id=lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				type_vocab_size
							UpperCAmelCase_						=				initializer_range
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				position_embedding_type
							UpperCAmelCase_						=				use_cache
							UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							if self.task == "multiple-choice":
									UpperCAmelCase_						=				{0: "batch", 1: "choice", 2: "sequence"}
							else:
									UpperCAmelCase_						=				{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]     )

