






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


lowerCamelCase	:							Optional[Any]				=logging.getLogger(__name__)

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

lowerCamelCase	:							Tuple				={
    '''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
lowerCamelCase	:							Tuple				={
    '''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
}
lowerCamelCase	:							str				=sorted(arg_to_scheduler.keys())
lowerCamelCase	:							Optional[Any]				='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class 			__a  (    pl.LightningModule       ):







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   argparse.Namespace							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]="base"							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   int=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   **SCREAMING_SNAKE_CASE					:   List[str]							,   ):


      '''simple docstring'''






      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						)
      UpperCamelCase__			:   Optional[int]       				=  0
      UpperCamelCase__			:   Optional[int]       				=  Path(self.hparams.output_dir						)
      UpperCamelCase__			:   str       				=  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           UpperCamelCase__			:   List[str]       				=  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:
           UpperCamelCase__			:   PretrainedConfig       				=  config

      UpperCamelCase__			:   str       				=  ("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:
           UpperCamelCase__			:   int       				=  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:
           UpperCamelCase__			:   PreTrainedTokenizer       				=  tokenizer
      UpperCamelCase__			:   int       				=  MODEL_MODES[mode]
      if model is None:
           UpperCamelCase__			:   List[str]       				=  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:
           UpperCamelCase__			:   List[str]       				=  model







 def 					__lowercase					(		self					:   Union[str, Any]							,   *SCREAMING_SNAKE_CASE					:   Dict							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  arg_to_scheduler[self.hparams.lr_scheduler]
      UpperCamelCase__			:   List[str]       				=  get_schedule_func(
          self.opt							,   num_warmup_steps=self.hparams.warmup_steps							,   num_training_steps=self.total_steps()						)
      UpperCamelCase__			:   Union[str, Any]       				=  {"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.model
      UpperCamelCase__			:   Tuple       				=  ["bias", "LayerNorm.weight"]
      UpperCamelCase__			:   Dict       				=  [
          {
              "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:
           UpperCamelCase__			:   List[str]       				=  Adafactor(
               SCREAMING_SNAKE_CASE							,   lr=self.hparams.learning_rate							,   scale_parameter=SCREAMING_SNAKE_CASE							,   relative_step=SCREAMING_SNAKE_CASE						)

      else:
           UpperCamelCase__			:   int       				=  AdamW(
               SCREAMING_SNAKE_CASE							,   lr=self.hparams.learning_rate							,   eps=self.hparams.adam_epsilon						)
      UpperCamelCase__			:   Optional[int]       				=  optimizer

      UpperCamelCase__			:   str       				=  self.get_lr_scheduler()

      return [optimizer], [scheduler]







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self.validation_step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      return self.validation_end(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  max(1							,   self.hparams.gpus						)  # TODO: consider num_tpu_cores
      UpperCamelCase__			:   List[Any]       				=  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					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      if stage == "test":
           UpperCamelCase__			:   str       				=  len(self.test_dataloader().dataset						)
      else:
           UpperCamelCase__			:   int       				=  self.get_dataloader("train"							,   self.hparams.train_batch_size							,   shuffle=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[Any]       				=  len(self.train_dataloader().dataset						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   bool = False						):


      '''simple docstring'''






      raise NotImplementedError("You must implement this for your task"						)







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return self.train_loader







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      return self.get_dataloader("dev"							,   self.hparams.eval_batch_size							,   shuffle=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      return self.get_dataloader("test"							,   self.hparams.eval_batch_size							,   shuffle=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      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					:   Dict							,   SCREAMING_SNAKE_CASE					:   Dict[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.output_dir.joinpath("best_tfmr"						)
      UpperCamelCase__			:   Tuple       				=  self.step_count
      self.model.save_pretrained(SCREAMING_SNAKE_CASE						)
      self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE						)







 @staticmethod
 def 					__lowercase					(		SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      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 			__a  (    pl.Callback       ):







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      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 			__a  (    pl.Callback       ):







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
                print(SCREAMING_SNAKE_CASE						)







class 			__a  (    pl.Callback       ):







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  trainer.lr_schedulers[0]["scheduler"]
      UpperCamelCase__			:   Any       				=  {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					:   Any							,   SCREAMING_SNAKE_CASE					:   pl.Trainer							,   SCREAMING_SNAKE_CASE					:   pl.LightningModule						):


      '''simple docstring'''






      rank_zero_info("***** Validation results *****"						)
      UpperCamelCase__			:   Dict       				=  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					:   Any							,   SCREAMING_SNAKE_CASE					:   pl.Trainer							,   SCREAMING_SNAKE_CASE					:   pl.LightningModule						):


      '''simple docstring'''






      rank_zero_info("***** Test results *****"						)
      UpperCamelCase__			:   str       				=  trainer.callback_metrics
      # Log and save results to file
      UpperCamelCase__			:   int       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase=None					,    __lowerCAmelCase=True					,    __lowerCAmelCase=[]					,    __lowerCAmelCase=None					,    __lowerCAmelCase=None					,    **__lowerCAmelCase					,    ) ->	List[str]:
     pl.seed_everything(args.seed			)

     # init model
     UpperCamelCase__			:   Union[str, Any]       				=  Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=__lowerCAmelCase			)

     # add custom checkpoints
     if checkpoint_callback is None:
          UpperCamelCase__			:   Any       				=  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:
          UpperCamelCase__			:   Optional[Any]       				=  LoggingCallback()

     UpperCamelCase__			:   Optional[Any]       				=  {}

     if args.fpaa:
          UpperCamelCase__			:   List[str]       				=  16

     if args.gpus > 1:
          UpperCamelCase__			:   Optional[int]       				=  "auto"
          UpperCamelCase__			:   List[str]       				=  "ddp"

     UpperCamelCase__			:   Optional[Any]       				=  args.accumulate_grad_batches
     UpperCamelCase__			:   int       				=  None
     UpperCamelCase__			:   int       				=  "auto"

     UpperCamelCase__			:   List[Any]       				=  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


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Dict				={
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[Any]       				=  max_target_positions

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE							,   bos_token_id=SCREAMING_SNAKE_CASE							,   eos_token_id=SCREAMING_SNAKE_CASE							,   decoder_start_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )