

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


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


  """simple docstring"""




  a__					       =  "summarization"
  a__					       =  ["loss"]
  a__					       =  ROUGE_KEYS
  a__					       =  "rouge2"


  def __init__(				self	,       __snake_case	,       **__snake_case):
   if hparams.sortish_sampler and hparams.gpus > 1:
    _UpperCamelCase   :		str			=						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__(__A	,       num_labels=__A	,       mode=self.mode	,       **__A)
   use_task_specific_params(self.model	,       'summarization')
   save_git_info(self.hparams.output_dir)
   _UpperCamelCase   :		Optional[int]			=						Path(self.output_dir) / 'metrics.json'
   _UpperCamelCase   :		Any			=						Path(self.output_dir) / 'hparams.pkl'
   pickle_save(self.hparams	,       self.hparams_save_path)
   _UpperCamelCase   :		List[str]			=						0
   _UpperCamelCase   :		Any			=						defaultdict(__A)
   _UpperCamelCase   :		int			=						self.config.model_type
   _UpperCamelCase   :		Optional[int]			=						self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

   _UpperCamelCase   :		List[Any]			=						{
       'data_dir': self.hparams.data_dir,
       'max_source_length': self.hparams.max_source_length,
       'prefix': self.model.config.prefix or '',
   }
   _UpperCamelCase   :		Dict			=						{
       'train': self.hparams.n_train,
       'val': self.hparams.n_val,
       'test': self.hparams.n_test,
   }
   _UpperCamelCase   :		Union[str, Any]			=						{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

   _UpperCamelCase   :		Optional[int]			=						{
       '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   :		int			=						get_git_info()['repo_sha']
   _UpperCamelCase   :		Optional[int]			=						hparams.num_workers
   _UpperCamelCase   :		List[str]			=						None  # default to config
   if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,       __A):
    _UpperCamelCase   :		Union[str, Any]			=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
    _UpperCamelCase   :		Union[str, Any]			=						self.decoder_start_token_id
   _UpperCamelCase   :		Optional[int]			=						(
       SeqaSeqDataset if hasattr(self.tokenizer	,       'prepare_seq2seq_batch') else LegacySeqaSeqDataset
   )
   _UpperCamelCase   :		Any			=						False
   _UpperCamelCase   :		Tuple			=						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   :		str			=						self.hparams.eval_max_gen_length
   else:
    _UpperCamelCase   :		int			=						self.model.config.max_length
   _UpperCamelCase   :		Dict			=						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Dict			=						{
       k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
   }
   save_json(__A	,       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   :		Dict			=						True
   return readable_batch


  def 		A__					(				self	,       __snake_case	,       **__snake_case):
   return self.model(__A	,       **__A)


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Any			=						self.tokenizer.batch_decode(
       __A	,       skip_special_tokens=__A	,       clean_up_tokenization_spaces=__A)
   return lmap(str.strip	,       __A)


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Optional[int]			=						self.tokenizer.pad_token_id
   _UpperCamelCase	,		_UpperCamelCase   :		Any			=						batch['input_ids'], batch['attention_mask']
   _UpperCamelCase   :		Optional[int]			=						batch['labels']
   if isinstance(self.model	,       __A):
    _UpperCamelCase   :		str			=						self.model._shift_right(__A)
   else:
    _UpperCamelCase   :		Optional[int]			=						shift_tokens_right(__A	,       __A)
   if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
    _UpperCamelCase   :		List[Any]			=						decoder_input_ids
    self.save_readable_batch(__A)

   _UpperCamelCase   :		Dict			=						self(__A	,       attention_mask=__A	,       decoder_input_ids=__A	,       use_cache=__A)
   _UpperCamelCase   :		Optional[Any]			=						outputs['logits']
   if self.hparams.label_smoothing == 0:
    # Same behavior as modeling_bart.py, besides ignoring pad_token_id
    _UpperCamelCase   :		str			=						nn.CrossEntropyLoss(ignore_index=__A)

    assert lm_logits.shape[-1] == self.vocab_size
    _UpperCamelCase   :		Tuple			=						ce_loss_fct(lm_logits.view(-1	,       lm_logits.shape[-1])	,       tgt_ids.view(-1))
   else:
    _UpperCamelCase   :		List[Any]			=						nn.functional.log_softmax(__A	,       dim=-1)
    _UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						label_smoothed_nll_loss(
        __A	,       __A	,       self.hparams.label_smoothing	,       ignore_index=__A)
   return (loss,)


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


  def 		A__					(				self	,       __snake_case	,       __snake_case):
   _UpperCamelCase   :		Optional[Any]			=						self._step(__A)

   _UpperCamelCase   :		List[str]			=						dict(zip(self.loss_names	,       __A))
   # tokens per batch
   _UpperCamelCase   :		int			=						batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
   _UpperCamelCase   :		List[Any]			=						batch['input_ids'].shape[0]
   _UpperCamelCase   :		Union[str, Any]			=						batch['input_ids'].eq(self.pad).sum()
   _UpperCamelCase   :		Tuple			=						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	,       __snake_case	,       __snake_case):
   return self._generative_step(__A)


  def 		A__					(				self	,       __snake_case	,       __snake_case="val"):
   self.step_count += 1
   _UpperCamelCase   :		List[Any]			=						{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
   _UpperCamelCase   :		Optional[int]			=						losses['loss']
   _UpperCamelCase   :		Optional[int]			=						{
       k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['gen_time', 'gen_len']
   }
   _UpperCamelCase   :		int			=						(
       generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
   )
   _UpperCamelCase   :		int			=						torch.tensor(__A).type_as(__A)
   generative_metrics.update({k: v.item() for k, v in losses.items()})
   losses.update(__A)
   _UpperCamelCase   :		int			=						{f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
   _UpperCamelCase   :		List[str]			=						self.step_count
   self.metrics[prefix].append(__A)  # callback writes this to self.metrics_save_path
   _UpperCamelCase   :		Any			=						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	,       __snake_case	,       __snake_case):
   return calculate_rouge(__A	,       __A)


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Dict			=						time.time()

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


  def 		A__					(				self	,       __snake_case	,       __snake_case):
   return self._generative_step(__A)


  def 		A__					(				self	,       __snake_case):
   return self.validation_epoch_end(__A	,       prefix='test')


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Union[str, Any]			=						self.n_obs[type_path]
   _UpperCamelCase   :		List[Any]			=						self.target_lens[type_path]
   _UpperCamelCase   :		str			=						self.dataset_class(
       self.tokenizer	,       type_path=__A	,       n_obs=__A	,       max_target_length=__A	,       **self.dataset_kwargs	,       )
   return dataset


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = False):
   _UpperCamelCase   :		Dict			=						self.get_dataset(__A)

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

   elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
    _UpperCamelCase   :		Union[str, Any]			=						dataset.make_dynamic_sampler(
        self.hparams.max_tokens_per_batch	,       distributed=self.hparams.gpus > 1)
    return DataLoader(
        __A	,       batch_sampler=__A	,       collate_fn=dataset.collate_fn	,       num_workers=self.num_workers	,       )
   else:
    return DataLoader(
        __A	,       batch_size=__A	,       collate_fn=dataset.collate_fn	,       shuffle=__A	,       num_workers=self.num_workers	,       sampler=__A	,       )


  def 		A__					(				self):
   _UpperCamelCase   :		Any			=						self.get_dataloader('train'	,       batch_size=self.hparams.train_batch_size	,       shuffle=__A)
   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__					(				__snake_case	,       __snake_case):
   BaseTransformer.add_model_specific_args(__A	,       __A)
   add_generic_args(__A	,       __A)
   parser.add_argument(
       '--max_source_length'	,       default=10_24	,       type=__A	,       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=__A	,       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=1_42	,       type=__A	,       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=1_42	,       type=__A	,       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=__A)
   parser.add_argument('--overwrite_output_dir'	,       action='store_true'	,       default=__A)
   parser.add_argument('--max_tokens_per_batch'	,       type=__A	,       default=__A)
   parser.add_argument('--logger_name'	,       type=__A	,       choices=['default', 'wandb', 'wandb_shared']	,       default='default')
   parser.add_argument('--n_train'	,       type=__A	,       default=-1	,       required=__A	,       help='# examples. -1 means use all.')
   parser.add_argument('--n_val'	,       type=__A	,       default=5_00	,       required=__A	,       help='# examples. -1 means use all.')
   parser.add_argument('--n_test'	,       type=__A	,       default=-1	,       required=__A	,       help='# examples. -1 means use all.')
   parser.add_argument(
       '--task'	,       type=__A	,       default='summarization'	,       required=__A	,       help='# examples. -1 means use all.')
   parser.add_argument('--label_smoothing'	,       type=__A	,       default=0.0	,       required=__A)
   parser.add_argument('--src_lang'	,       type=__A	,       default=''	,       required=__A)
   parser.add_argument('--tgt_lang'	,       type=__A	,       default=''	,       required=__A)
   parser.add_argument('--eval_beams'	,       type=__A	,       default=__A	,       required=__A)
   parser.add_argument(
       '--val_metric'	,       type=__A	,       default=__A	,       required=__A	,       choices=['bleu', 'rouge2', 'loss', None])
   parser.add_argument('--eval_max_gen_length'	,       type=__A	,       default=__A	,       help='never generate more than n tokens')
   parser.add_argument('--save_top_k'	,       type=__A	,       default=1	,       required=__A	,       help='How many checkpoints to save')
   parser.add_argument(
       '--early_stopping_patience'	,       type=__A	,       default=-1	,       required=__A	,       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 	lowercase    (				_lowercase							):


  """simple docstring"""




  a__					       =  "translation"
  a__					       =  ["loss"]
  a__					       =  ["bleu"]
  a__					       =  "bleu"


  def __init__(				self	,       __snake_case	,       **__snake_case):
   super().__init__(__A	,       **__A)
   _UpperCamelCase   :		str			=						hparams.src_lang
   _UpperCamelCase   :		Dict			=						hparams.tgt_lang






  def 		A__					(				self	,       __snake_case	,       __snake_case):
   return calculate_bleu(__A	,       __A)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Dict=None	)						->				str:




 '''simple docstring'''




 Path(args.output_dir	).mkdir(exist_ok=_lowercase	)
 check_output_dir(_lowercase							,       expected_items=3	)

 if model is None:
  if "summarization" in args.task:
   _UpperCamelCase   :		Dict			=						SummarizationModule(_lowercase	)
  else:
   _UpperCamelCase   :		int			=						TranslationModule(_lowercase	)
 _UpperCamelCase   :		Optional[int]			=						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   :		Tuple			=						True  # don't pollute wandb logs unnecessarily
 elif args.logger_name == "wandb":
  from pytorch_lightning.loggers import WandbLogger

  _UpperCamelCase   :		Union[str, Any]			=						os.environ.get('WANDB_PROJECT'							,       _lowercase	)
  _UpperCamelCase   :		Any			=						WandbLogger(name=model.output_dir.name							,       project=_lowercase	)

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

  _UpperCamelCase   :		Dict			=						WandbLogger(name=model.output_dir.name							,       project=F'''hf_{dataset}'''	)

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

 _UpperCamelCase   :		Dict			=						args.val_metric == 'loss'
 _UpperCamelCase   :		List[str]			=						generic_train(
     _lowercase							,       _lowercase							,       logging_callback=SeqaSeqLoggingCallback()							,       checkpoint_callback=get_checkpoint_callback(
         args.output_dir							,       model.val_metric							,       args.save_top_k							,       _lowercase	)							,       early_stopping_callback=_lowercase							,       logger=_lowercase							,       )
 pickle_save(model.hparams							,       model.output_dir / 'hparams.pkl'	)
 if not args.do_predict:
  return model

 _UpperCamelCase   :		Optional[int]			=						''
 _UpperCamelCase   :		List[Any]			=						sorted(glob.glob(os.path.join(args.output_dir							,       '*.ckpt'	)							,       recursive=_lowercase	)	)
 if checkpoints:
  _UpperCamelCase   :		Optional[Any]			=						checkpoints[-1]
  _UpperCamelCase   :		Tuple			=						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__":
       lowerCAmelCase__								=		argparse.ArgumentParser()
       lowerCAmelCase__								=		pl.Trainer.add_argparse_args(parser)
       lowerCAmelCase__								=		SummarizationModule.add_model_specific_args(parser, os.getcwd())

       lowerCAmelCase__								=		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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="absolute"	,       __snake_case=True	,       __snake_case=None	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		int			=						vocab_size
			_UpperCamelCase   :		Optional[Any]			=						hidden_size
			_UpperCamelCase   :		Optional[Any]			=						num_hidden_layers
			_UpperCamelCase   :		List[str]			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		Optional[Any]			=						intermediate_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_dropout_prob
			_UpperCamelCase   :		Tuple			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						max_position_embeddings
			_UpperCamelCase   :		str			=						type_vocab_size
			_UpperCamelCase   :		Optional[Any]			=						initializer_range
			_UpperCamelCase   :		List[str]			=						layer_norm_eps
			_UpperCamelCase   :		Any			=						position_embedding_type
			_UpperCamelCase   :		Any			=						use_cache
			_UpperCamelCase   :		Any			=						classifier_dropout







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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




