

'''simple docstring'''



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


UpperCAmelCase :       Any			   =     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase :       Optional[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
UpperCAmelCase :       Optional[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
}
UpperCAmelCase :       Optional[int]			   =     sorted(arg_to_scheduler.keys())
UpperCAmelCase :       List[str]			   =     '{' + ', '.join(arg_to_scheduler_choices) + '}'







class 						lowerCAmelCase__   (      pl.LightningModule					):



 """simple docstring"""



 def __init__(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       argparse.Namespace					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       int="base"					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=None					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=None					,							**__SCREAMING_SNAKE_CASE					:       Dict					,							)  -> 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			)
      __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       UpperCAmelCase__		(		self					:       List[Any]					,							*__SCREAMING_SNAKE_CASE					:       List[Any]					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      __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       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __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       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict			)  -> Union[str, Any]:


      """simple docstring"""




      return self.validation_step(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      return self.validation_end(__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      __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       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      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       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       bool = False			)  -> Any:


      """simple docstring"""




      raise NotImplementedError("""You must implement this for your task"""			)
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return self.train_loader
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[str]:


      """simple docstring"""




      return self.get_dataloader("""dev"""					,							self.hparams.eval_batch_size					,							shuffle=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Union[str, Any]:


      """simple docstring"""




      return self.get_dataloader("""test"""					,							self.hparams.eval_batch_size					,							shuffle=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> List[str]:


      """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       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Dict[str, Any]			)  -> None:


      """simple docstring"""




      __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       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> Optional[int]:


      """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 						lowerCAmelCase__   (      pl.Callback					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> 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 						lowerCAmelCase__   (      pl.Callback					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> int:


      """simple docstring"""




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







class 						lowerCAmelCase__   (      pl.Callback					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> List[str]:


      """simple docstring"""




      __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       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       pl.Trainer					,							__SCREAMING_SNAKE_CASE					:       pl.LightningModule			)  -> Union[str, Any]:


      """simple docstring"""




      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       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       pl.Trainer					,							__SCREAMING_SNAKE_CASE					:       pl.LightningModule			)  -> int:


      """simple docstring"""




      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    a__       (  a__      ,    a__						):




     """simple docstring"""


     parser.add_argument(
         """--output_dir"""      ,    default=str(Path(a__						).parent / """test_run""" / """model_checkpoints"""						)      ,    type=a__      ,    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=a__      ,    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=a__						)
     parser.add_argument("""--max_grad_norm"""      ,    dest="""gradient_clip_val"""      ,    default=1.0      ,    type=a__      ,    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=a__      ,    default=1      ,    help="""Number of updates steps to accumulate before performing a backward/update pass."""      ,    )
     parser.add_argument("""--seed"""      ,    type=a__      ,    default=42      ,    help="""random seed for initialization"""						)
     parser.add_argument(
         """--data_dir"""      ,    default=str(Path(a__						).parent / """test_run""" / """dummy-train-data"""						)      ,    type=a__      ,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""      ,    )







def    a__       (  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=a__						)

     # 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(a__						)
     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(
         a__      ,    weights_summary=a__      ,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,    logger=a__      ,    val_check_interval=1      ,    num_sanity_val_steps=2      ,    **a__      ,    )

     if args.do_train:
          trainer.fit(a__						)

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









'''simple docstring'''



from itertools import count







def    a__       (  a__ = 50						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [1] * min_block_length

     for n in count(a__						):
          fill_count_functions.append(1						)

          for block_length in range(a__      ,    n + 1						):
               for block_start in range(n - block_length						):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 1_00_00_00:
               break

     return n


if __name__ == "__main__":
  print(f"""{solution() = }""")






