


"""simple docstring"""



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     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		List[Any]:


    """simple docstring"""





    snake_case       : str    =       filter(lambda lowercase					: p.requires_grad		,				model.parameters()					)
    snake_case       : List[str]    =       sum([np.prod(p.size()					) for p in model_parameters]					)
    return params


__snake_case       =       logging.getLogger(__name__)







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Union[str, Any]					)      ->		Tuple:


    """simple docstring"""





    if metric == "rouge2":
        snake_case       : List[Any]    =       "{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        snake_case       : Dict    =       "{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        snake_case       : Union[str, Any]    =       "{val_avg_em:.4f}-{step_count}"
    elif metric == "loss":
        snake_case       : Any    =       "{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."					)

    snake_case       : Dict    =       ModelCheckpoint(
        dirpath=lowercase		,				filename=lowercase		,				monitor=F'val_{metric}'		,				mode="max"		,				save_top_k=1		,				every_n_epochs=1		,				)

    return checkpoint_callback







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    Union[str, Any]					)      ->		Union[str, Any]:


    """simple docstring"""





    return EarlyStopping(
        monitor=F'val_{metric}'		,				mode="min" if "loss" in metric else "max"		,				patience=lowercase		,				verbose=lowercase		,				)







class _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




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







   @rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__=True		)      ->						None:

       '''simple docstring'''




       logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'		)
       snake_case       : Optional[int]    =       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
       snake_case       : Any    =       Path(pl_module.hparams.output_dir		)
       if type_path == "test":
           snake_case       : Tuple    =       od / "test_results.txt"
           snake_case       : 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.
           snake_case       : Optional[Any]    =       od / F'{type_path}_results/{trainer.global_step:05d}.txt'
           snake_case       : Dict    =       od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
           results_file.parent.mkdir(exist_ok=UpperCamelCase__		)
           generations_file.parent.mkdir(exist_ok=UpperCamelCase__		)
       with open(UpperCamelCase__		,      "a+"		) as writer:
           for key in sorted(UpperCamelCase__		):
               if key in ["log", "progress_bar", "preds"]:
                   continue
               snake_case       : Dict    =       metrics[key]
               if isinstance(UpperCamelCase__		,      torch.Tensor		):
                   snake_case       : Dict    =       val.item()
               snake_case       : List[str]    =       F'{key}: {val:.6f}\n'
               writer.write(UpperCamelCase__		)

       if not save_generations:
           return

       if "preds" in metrics:
           snake_case       : Optional[int]    =       "\n".join(metrics["preds"]		)
           generations_file.open("w+"		).write(UpperCamelCase__		)







   @rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       try:
           snake_case       : Tuple    =       pl_module.model.model.num_parameters()
       except AttributeError:
           snake_case       : Tuple    =       pl_module.model.num_parameters()

       snake_case       : List[Any]    =       count_trainable_parameters(UpperCamelCase__		)
       # 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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       save_json(pl_module.metrics		,      pl_module.metrics_save_path		)
       return self._write_logs(UpperCamelCase__		,      UpperCamelCase__		,      "test"		)







   @rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





