
'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase							:      Tuple		 =   {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:      Dict		 =   [
					    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AutoformerForPrediction',
					    'AutoformerModel',
					    'AutoformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					__lowercase							:      List[str]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{val_avg_em:.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.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   return EarlyStopping(
       monitor=F"""val_{metric}""",	mode='min' if 'loss' in metric else 'max',	patience=lowerCAmelCase_,	verbose=lowerCAmelCase_,	)




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	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.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
        generations_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
     with open(lowerCAmelCase_					,  'a+'     ) as writer:
        for key in sorted(lowerCAmelCase_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

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






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	count_trainable_parameters(lowerCAmelCase_     )
     # 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 :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

