






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_SCREAMING_SNAKE_CASE					:  int             =      logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_SCREAMING_SNAKE_CASE					:  List[Any]             =      {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_SCREAMING_SNAKE_CASE					:  int             =      {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_SCREAMING_SNAKE_CASE					:  Any             =      sorted(arg_to_scheduler.keys())
_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '{' + ', '.join(arg_to_scheduler_choices) + '}'






class 	UpperCAmelCase__		(	pl.LightningModule	):






  """simple docstring"""


  def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Optional[int]=None     ,       __lowerCamelCase						:		int="base"     ,       __lowerCamelCase						:		Tuple=None     ,       __lowerCamelCase						:		str=None     ,       __lowerCamelCase						:		List[Any]=None     ,       **__lowerCamelCase						:		str     ,       )		->							List[Any]:
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(SCREAMING_SNAKE_CASE_ )
       SCREAMING_SNAKE_CASE__   =   0
       SCREAMING_SNAKE_CASE__   =   Path(self.hparams.output_dir )
       SCREAMING_SNAKE_CASE__   =   self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
            SCREAMING_SNAKE_CASE__   =   AutoConfig.from_pretrained(
                self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,       **({'''num_labels''': num_labels} if num_labels is not None else {})     ,       cache_dir=SCREAMING_SNAKE_CASE_     ,       **SCREAMING_SNAKE_CASE_     ,       )
       else:
            SCREAMING_SNAKE_CASE__   =   config

       SCREAMING_SNAKE_CASE__   =   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
       for p in extra_model_params:
            if getattr(self.hparams     ,       SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_ ):
                 assert hasattr(self.config     ,       SCREAMING_SNAKE_CASE_ ), f'''model config doesn\'t have a `{p}` attribute'''
                 setattr(self.config     ,       SCREAMING_SNAKE_CASE_     ,       getattr(self.hparams     ,       SCREAMING_SNAKE_CASE_ ) )

       if tokenizer is None:
            SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,       cache_dir=SCREAMING_SNAKE_CASE_     ,       )
       else:
            SCREAMING_SNAKE_CASE__   =   tokenizer
       SCREAMING_SNAKE_CASE__   =   MODEL_MODES[mode]
       if model is None:
            SCREAMING_SNAKE_CASE__   =   self.model_type.from_pretrained(
                self.hparams.model_name_or_path     ,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path )     ,       config=self.config     ,       cache_dir=SCREAMING_SNAKE_CASE_     ,       )
       else:
            SCREAMING_SNAKE_CASE__   =   model






  def 				lowercase_		(							self						:		Optional[Any]     ,       *__lowerCamelCase						:		Tuple     ,       **__lowerCamelCase						:		List[Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_     ,       **SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		str )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   arg_to_scheduler[self.hparams.lr_scheduler]
       SCREAMING_SNAKE_CASE__   =   get_schedule_func(
           self.opt     ,       num_warmup_steps=self.hparams.warmup_steps     ,       num_training_steps=self.total_steps() )
       SCREAMING_SNAKE_CASE__   =   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
       return scheduler






  def 				lowercase_		(							self						:		Dict )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.model
       SCREAMING_SNAKE_CASE__   =   ["""bias""", """LayerNorm.weight"""]
       SCREAMING_SNAKE_CASE__   =   [
           {
               """params""": [
                   p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
               ],  # check this named paramters
               """weight_decay""": self.hparams.weight_decay,
           },
           {
               """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
               """weight_decay""": 0.0,
           },
       ]
       if self.hparams.adafactor:
            SCREAMING_SNAKE_CASE__   =   Adafactor(
                SCREAMING_SNAKE_CASE_     ,       lr=self.hparams.learning_rate     ,       scale_parameter=SCREAMING_SNAKE_CASE_     ,       relative_step=SCREAMING_SNAKE_CASE_ )

       else:
            SCREAMING_SNAKE_CASE__   =   AdamW(
                SCREAMING_SNAKE_CASE_     ,       lr=self.hparams.learning_rate     ,       eps=self.hparams.adam_epsilon )
       SCREAMING_SNAKE_CASE__   =   optimizer

       SCREAMING_SNAKE_CASE__   =   self.get_lr_scheduler()

       return [optimizer], [scheduler]






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		List[str] )		->							Dict:
       return self.validation_step(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Union[str, Any] )		->							Union[str, Any]:
       return self.validation_end(SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		List[Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   max(1     ,       self.hparams.gpus )  # TODO: consider num_tpu_cores
       SCREAMING_SNAKE_CASE__   =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Tuple )		->							Any:
       if stage == "test":
            SCREAMING_SNAKE_CASE__   =   len(self.test_dataloader().dataset )
       else:
            SCREAMING_SNAKE_CASE__   =   self.get_dataloader('''train'''     ,       self.hparams.train_batch_size     ,       shuffle=SCREAMING_SNAKE_CASE_ )
            SCREAMING_SNAKE_CASE__   =   len(self.train_dataloader().dataset )






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Optional[Any] = False )		->							str:
       raise NotImplementedError('''You must implement this for your task''' )






  def 				lowercase_		(							self						:		Dict )		->							Union[str, Any]:
       return self.train_loader






  def 				lowercase_		(							self						:		Tuple )		->							Dict:
       return self.get_dataloader('''dev'''     ,       self.hparams.eval_batch_size     ,       shuffle=SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		Any )		->							List[str]:
       return self.get_dataloader('''test'''     ,       self.hparams.eval_batch_size     ,       shuffle=SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		int )		->							Dict:
       return os.path.join(
           self.hparams.data_dir     ,       '''cached_{}_{}_{}'''.format(
               SCREAMING_SNAKE_CASE_     ,       list(filter(SCREAMING_SNAKE_CASE_     ,       self.hparams.model_name_or_path.split('''/''' ) ) ).pop()     ,       str(self.hparams.max_seq_length )     ,       )     ,       )






  @pl.utilities.rank_zero_only
  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict )		->							None:
       SCREAMING_SNAKE_CASE__   =   self.output_dir.joinpath('''best_tfmr''' )
       SCREAMING_SNAKE_CASE__   =   self.step_count
       self.model.save_pretrained(SCREAMING_SNAKE_CASE_ )
       self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )







  @staticmethod
  def 				lowercase_		(							__lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		str )		->							int:
       parser.add_argument(
           '''--model_name_or_path'''     ,       default=SCREAMING_SNAKE_CASE_     ,       type=SCREAMING_SNAKE_CASE_     ,       required=SCREAMING_SNAKE_CASE_     ,       help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,       )
       parser.add_argument(
           '''--config_name'''     ,       default=''''''     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Pretrained config name or path if not the same as model_name''' )
       parser.add_argument(
           '''--tokenizer_name'''     ,       default=SCREAMING_SNAKE_CASE_     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Pretrained tokenizer name or path if not the same as model_name'''     ,       )
       parser.add_argument(
           '''--cache_dir'''     ,       default=str(Path(SCREAMING_SNAKE_CASE_ ).parent / '''test_run''' / '''cache''' )     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,       )
       parser.add_argument(
           '''--encoder_layerdrop'''     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,       )
       parser.add_argument(
           '''--decoder_layerdrop'''     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,       )
       parser.add_argument(
           '''--dropout'''     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Dropout probability (Optional). Goes into model.config'''     ,       )
       parser.add_argument(
           '''--attention_dropout'''     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Attention dropout probability (Optional). Goes into model.config'''     ,       )
       parser.add_argument('''--learning_rate'''     ,       default=5e-5     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''The initial learning rate for Adam.''' )
       parser.add_argument(
           '''--lr_scheduler'''     ,       default='''linear'''     ,       choices=SCREAMING_SNAKE_CASE_     ,       metavar=SCREAMING_SNAKE_CASE_     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Learning rate scheduler'''     ,       )
       parser.add_argument('''--weight_decay'''     ,       default=0.0     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Weight decay if we apply some.''' )
       parser.add_argument('''--adam_epsilon'''     ,       default=1e-8     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Epsilon for Adam optimizer.''' )
       parser.add_argument('''--warmup_steps'''     ,       default=0     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''Linear warmup over warmup_steps.''' )
       parser.add_argument('''--num_workers'''     ,       default=4     ,       type=SCREAMING_SNAKE_CASE_     ,       help='''kwarg passed to DataLoader''' )
       parser.add_argument('''--num_train_epochs'''     ,       dest='''max_epochs'''     ,       default=3     ,       type=SCREAMING_SNAKE_CASE_ )
       parser.add_argument('''--train_batch_size'''     ,       default=32     ,       type=SCREAMING_SNAKE_CASE_ )
       parser.add_argument('''--eval_batch_size'''     ,       default=32     ,       type=SCREAMING_SNAKE_CASE_ )
       parser.add_argument('''--adafactor'''     ,       action='''store_true''' )






class 	UpperCAmelCase__		(	pl.Callback	):






  """simple docstring"""


  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Tuple )		->							Optional[Any]:
       if (
           trainer.is_global_zero and trainer.global_rank == 0
       ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
            pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 	UpperCAmelCase__		(	pl.Callback	):






  """simple docstring"""


  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Union[str, Any] )		->							Optional[int]:
       # print(pl_module.model.rag)
       for name, param in pl_module.model.rag.named_parameters():
            if param.grad is None:
                 print(SCREAMING_SNAKE_CASE_ )






class 	UpperCAmelCase__		(	pl.Callback	):






  """simple docstring"""


  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   trainer.lr_schedulers[0]["""scheduler"""]
       SCREAMING_SNAKE_CASE__   =   {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
       pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_ )






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Any )		->							Tuple:
       rank_zero_info('''***** Validation results *****''' )
       SCREAMING_SNAKE_CASE__   =   trainer.callback_metrics
       # Log results
       for key in sorted(SCREAMING_SNAKE_CASE_ ):
            if key not in ["log", "progress_bar"]:
                 rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_     ,       str(metrics[key] ) ) )







  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[Any] )		->							Tuple:
       rank_zero_info('''***** Test results *****''' )
       SCREAMING_SNAKE_CASE__   =   trainer.callback_metrics
       # Log and save results to file
       SCREAMING_SNAKE_CASE__   =   os.path.join(pl_module.hparams.output_dir     ,       '''test_results.txt''' )
       with open(SCREAMING_SNAKE_CASE_     ,       '''w''' ) as writer:
            for key in sorted(SCREAMING_SNAKE_CASE_ ):
                 if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_     ,       str(metrics[key] ) ) )
                      writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_     ,       str(metrics[key] ) ) )



def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     parser.add_argument(
         '''--output_dir'''				,		default=str(Path(lowerCAmelCase__    ).parent / '''test_run''' / '''model_checkpoints'''    )				,		type=lowerCAmelCase__				,		help='''The output directory where the model predictions and checkpoints will be written.'''				,		)
     parser.add_argument(
         '''--fp16'''				,		action='''store_true'''				,		help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,		)

     parser.add_argument(
         '''--fp16_opt_level'''				,		type=lowerCAmelCase__				,		default='''O2'''				,		help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )				,		)
     parser.add_argument('''--n_tpu_cores'''				,		dest='''tpu_cores'''				,		type=lowerCAmelCase__    )
     parser.add_argument('''--max_grad_norm'''				,		dest='''gradient_clip_val'''				,		default=1.0				,		type=lowerCAmelCase__				,		help='''Max gradient norm'''    )
     parser.add_argument('''--do_train'''				,		action='''store_true'''				,		help='''Whether to run training.'''    )
     parser.add_argument('''--do_predict'''				,		action='''store_true'''				,		help='''Whether to run predictions on the test set.'''    )
     parser.add_argument(
         '''--gradient_accumulation_steps'''				,		dest='''accumulate_grad_batches'''				,		type=lowerCAmelCase__				,		default=1				,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,		)
     parser.add_argument('''--seed'''				,		type=lowerCAmelCase__				,		default=42				,		help='''random seed for initialization'''    )
     parser.add_argument(
         '''--data_dir'''				,		default=str(Path(lowerCAmelCase__    ).parent / '''test_run''' / '''dummy-train-data'''    )				,		type=lowerCAmelCase__				,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,		)







def     UpperCAmelCase_       (						_A				,		_A				,		_A=None				,		_A=True				,		_A=[]				,		_A=None				,		_A=None				,		**_A				,		):





     '''simple docstring'''




     pl.seed_everything(args.seed    )

     # init model
     SCREAMING_SNAKE_CASE__   =   Path(model.hparams.output_dir    )
     odir.mkdir(exist_ok=lowerCAmelCase__    )

     # add custom checkpoints
     if checkpoint_callback is None:
          SCREAMING_SNAKE_CASE__   =   pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir				,		prefix='''checkpoint'''				,		monitor='''val_loss'''				,		mode='''min'''				,		save_top_k=1    )
     if early_stopping_callback:
          extra_callbacks.append(lowerCAmelCase__    )
     if logging_callback is None:
          SCREAMING_SNAKE_CASE__   =   LoggingCallback()

     SCREAMING_SNAKE_CASE__   =   {}

     if args.fpaa:
          SCREAMING_SNAKE_CASE__   =   16

     if args.gpus > 1:
          SCREAMING_SNAKE_CASE__   =   """auto"""
          SCREAMING_SNAKE_CASE__   =   """ddp"""

     SCREAMING_SNAKE_CASE__   =   args.accumulate_grad_batches
     SCREAMING_SNAKE_CASE__   =   None
     SCREAMING_SNAKE_CASE__   =   """auto"""

     SCREAMING_SNAKE_CASE__   =   pl.Trainer.from_argparse_args(
         lowerCAmelCase__				,		weights_summary=lowerCAmelCase__				,		callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,		logger=lowerCAmelCase__				,		val_check_interval=1				,		num_sanity_val_steps=2				,		**lowerCAmelCase__				,		)

     if args.do_train:
          trainer.fit(lowerCAmelCase__    )

     else:
          print('''RAG modeling tests with new set functions successfuly executed!'''    )
     return trainer





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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       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 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # 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.''' )


