





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json



def A_	(		A__				)  ->		str:
       a__						:	Any =				filter(lambda A__				: p.requires_grad		,      model.parameters()				)
       a__						:	Union[str, Any] =				sum([np.prod(p.size()				) for p in model_parameters]				)
       return params


lowercase      :       Union[str, Any]									=  logging.getLogger(__name__)



def A_	(		A__		,      A__				)  ->		int:
       if metric == "rouge2":
              a__						:	Any =				'{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a__						:	Tuple =				'{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a__						:	Union[str, Any] =				'{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a__						:	int =				'{val_avg_loss:.4f}-{step_count}'
       else:
              raise NotImplementedError(
                  F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
                  ' function.'				)

       a__						:	Tuple =				ModelCheckpoint(
           dirpath=A__		,      filename=A__		,      monitor=F'val_{metric}'		,      mode='max'		,      save_top_k=1		,      every_n_epochs=1		,      )

       return checkpoint_callback



def A_	(		A__		,      A__				)  ->		str:
       return EarlyStopping(
           monitor=F'val_{metric}'		,      mode='min' if 'loss' in metric else 'max'		,      patience=A__		,      verbose=A__		,      )





class  A__    (							pl.Callback	):




   """simple docstring"""



   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				{F'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
          pl_module.logger.log_metrics(lowercase)

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase=True)			->						None:







          '''simple docstring'''

          logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
          a__						:	Optional[Any] =				trainer.callback_metrics
          trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']})
          # Log results
          a__						:	Any =				Path(pl_module.hparams.output_dir)
          if type_path == "test":
                 a__						:	Dict =				od / 'test_results.txt'
                 a__						:	str =				od / 'test_generations.txt'
          else:
                 # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
                 # If people want this it will be easy enough to add back.
                 a__						:	Any =				od / F'{type_path}_results/{trainer.global_step:05d}.txt'
                 a__						:	Union[str, Any] =				od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
                 results_file.parent.mkdir(exist_ok=lowercase)
                 generations_file.parent.mkdir(exist_ok=lowercase)
          with open(lowercase					,  'a+') as writer:
                 for key in sorted(lowercase):
                        if key in ["log", "progress_bar", "preds"]:
                               continue
                        a__						:	Dict =				metrics[key]
                        if isinstance(lowercase					,  torch.Tensor):
                               a__						:	Optional[Any] =				val.item()
                        a__						:	Dict =				F'{key}: {val:.6f}\n'
                        writer.write(lowercase)

          if not save_generations:
                 return

          if "preds" in metrics:
                 a__						:	Tuple =				'\n'.join(metrics['preds'])
                 generations_file.open('w+').write(lowercase)

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						int:







          '''simple docstring'''

          try:
                 a__						:	int =				pl_module.model.model.num_parameters()
          except AttributeError:
                 a__						:	str =				pl_module.model.num_parameters()

          a__						:	Union[str, Any] =				count_trainable_parameters(lowercase)
          # mp stands for million parameters
          trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6})

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          save_json(pl_module.metrics					,  pl_module.metrics_save_path)
          return self._write_logs(lowercase					,  lowercase					,  'test')

   @rank_zero_only
   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          save_json(pl_module.metrics					,  pl_module.metrics_save_path)
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


