code: 
  
  
  """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   =	logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  UpperCAmelCase   =	{
      """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   =	{
      """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   =	sorted(arg_to_scheduler.keys())
  UpperCAmelCase   =	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  class   lowercase__							(				pl.LightningModule							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="base"			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      **SCREAMING_SNAKE_CASE			,      )     ->     int:
        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)
        _lowerCamelCase  :				Optional[Any]      =		0
        _lowerCamelCase  :				int      =		Path(self.hparams.output_dir)
        _lowerCamelCase  :				Dict      =		self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
           _lowerCamelCase  :				Union[str, Any]      =		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:
           _lowerCamelCase  :				PretrainedConfig      =		config
  
        _lowerCamelCase  :				Optional[int]      =		("""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:
           _lowerCamelCase  :				List[str]      =		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:
           _lowerCamelCase  :				PreTrainedTokenizer      =		tokenizer
        _lowerCamelCase  :				Optional[int]      =		MODEL_MODES[mode]
        if model is None:
           _lowerCamelCase  :				Optional[int]      =		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:
           _lowerCamelCase  :				Tuple      =		model
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        _lowerCamelCase  :				List[Any]      =		self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				int      =		arg_to_scheduler[self.hparams.lr_scheduler]
        _lowerCamelCase  :				Dict      =		get_schedule_func(
            self.opt			,      num_warmup_steps=self.hparams.warmup_steps			,      num_training_steps=self.total_steps())
        _lowerCamelCase  :				Any      =		{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				str      =		self.model
        _lowerCamelCase  :				List[str]      =		["""bias""", """LayerNorm.weight"""]
        _lowerCamelCase  :				int      =		[
            {
                """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                """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:
           _lowerCamelCase  :				List[Any]      =		Adafactor(
               SCREAMING_SNAKE_CASE			,      lr=self.hparams.learning_rate			,      scale_parameter=SCREAMING_SNAKE_CASE			,      relative_step=SCREAMING_SNAKE_CASE)
  
        else:
           _lowerCamelCase  :				str      =		AdamW(
               SCREAMING_SNAKE_CASE			,      lr=self.hparams.learning_rate			,      eps=self.hparams.adam_epsilon)
        _lowerCamelCase  :				str      =		optimizer
  
        _lowerCamelCase  :				str      =		self.get_lr_scheduler()
  
        return [optimizer], [scheduler]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        return self.validation_step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        return self.validation_end(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase  :				Optional[int]      =		max(1			,      self.hparams.gpus)  # TODO: consider num_tpu_cores
        _lowerCamelCase  :				int      =		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			,      SCREAMING_SNAKE_CASE)     ->     str:
        if mode == "test":
           _lowerCamelCase  :				Optional[Any]      =		len(self.test_dataloader().dataset)
        else:
           _lowerCamelCase  :				Optional[int]      =		self.get_dataloader("""train"""			,      self.hparams.train_batch_size			,      shuffle=SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				List[str]      =		len(self.train_dataloader().dataset)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = False)     ->     Union[str, Any]:
        raise NotImplementedError("""You must implement this for your task""")
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
        return self.train_loader
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        return self.get_dataloader("""dev"""			,      self.hparams.eval_batch_size			,      shuffle=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        return self.get_dataloader("""test"""			,      self.hparams.eval_batch_size			,      shuffle=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     List[Any]:
        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			,      SCREAMING_SNAKE_CASE)     ->     None:
        _lowerCamelCase  :				Dict      =		self.output_dir.joinpath("""best_tfmr""")
        _lowerCamelCase  :				str      =		self.step_count
        self.model.save_pretrained(SCREAMING_SNAKE_CASE)
        self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @staticmethod
     def 		UpperCamelCase_     (       SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[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=""""""			,      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   lowercase__							(				pl.Callback							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				List[str]      =		trainer.lr_schedulers[0]["""scheduler"""]
        _lowerCamelCase  :				List[Any]      =		{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			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        rank_zero_info("""***** Validation results *****""")
        _lowerCamelCase  :				Tuple      =		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			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        rank_zero_info("""***** Test results *****""")
        _lowerCamelCase  :				Dict      =		trainer.callback_metrics
        # Log and save results to file
        _lowerCamelCase  :				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 							_snake_case			(      __snake_case       :			Dict	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     parser.add_argument(
         """--output_dir"""	,   default=__snake_case	,   type=__snake_case	,   required=__snake_case	,   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=__snake_case	,   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=__snake_case      )
     parser.add_argument("""--max_grad_norm"""	,   dest="""gradient_clip_val"""	,   default=1.0	,   type=__snake_case	,   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=__snake_case	,   default=1	,   help="""Number of updates steps to accumulate before performing a backward/update pass."""	,   )
     parser.add_argument("""--seed"""	,   type=__snake_case	,   default=42	,   help="""random seed for initialization"""      )
     parser.add_argument(
         """--data_dir"""	,   default=__snake_case	,   type=__snake_case	,   required=__snake_case	,   help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""	,   )
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			BaseTransformer	,   __snake_case       :			argparse.Namespace	,   __snake_case       :			str=None	,   __snake_case       :			Optional[Any]=True	,   __snake_case       :			List[Any]=[]	,   __snake_case       :			Any=None	,   __snake_case       :			List[str]=None	,   **__snake_case       :			str	,   ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     pl.seed_everything(args.seed      )
  
     # init model
     _lowerCamelCase  :				Optional[int]      =		Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=__snake_case      )
  
     # add custom checkpoints
     if checkpoint_callback is None:
        _lowerCamelCase  :				Tuple      =		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(__snake_case      )
     if logging_callback is None:
        _lowerCamelCase  :				Optional[int]      =		LoggingCallback()
  
     _lowerCamelCase  :				List[str]      =		{}
  
     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
        _lowerCamelCase  :				List[Any]      =		16
        _lowerCamelCase  :				str      =		args.fpaa_opt_level
  
     if args.gpus > 1:
        _lowerCamelCase  :				Optional[Any]      =		"""ddp"""
  
     _lowerCamelCase  :				Optional[Any]      =		args.accumulate_grad_batches
     _lowerCamelCase  :				Union[str, Any]      =		extra_train_kwargs.get("""accelerator"""	,   __snake_case      )
     _lowerCamelCase  :				List[Any]      =		extra_train_kwargs.get("""profiler"""	,   __snake_case      )
  
     _lowerCamelCase  :				List[Any]      =		pl.Trainer.from_argparse_args(
         __snake_case	,   weights_summary=__snake_case	,   callbacks=[logging_callback] + extra_callbacks	,   logger=__snake_case	,   checkpoint_callback=__snake_case	,   **__snake_case	,   )
  
     if args.do_train:
        trainer.fit(__snake_case      )
  
     return trainer
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )
  
     for i, block in enumerate(gpt.blocks      ):
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )
  
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )
  
     torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  