


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=filter(lambda SCREAMING_SNAKE_CASE__				: p.requires_grad			,     model.parameters()				)
     __UpperCamelCase    							=sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A		       =						logging.getLogger(__name__)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					int				):
     if metric == "rouge2":
          __UpperCamelCase    							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase    							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase    							='{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.'				)

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     return EarlyStopping(
         monitor=F'val_{metric}'			,     mode='min' if 'loss' in metric else 'max'			,     patience=SCREAMING_SNAKE_CASE__			,     verbose=SCREAMING_SNAKE_CASE__			,     )






class     UpperCAmelCase__       (					pl.Callback		):



    """simple docstring"""






    def _a					(			self					,   A_					,   A_    )  ->       Dict:
         __UpperCamelCase    							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
         pl_module.logger.log_metrics(A_    )






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

         if not save_generations:
              return

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






    @rank_zero_only
    def _a					(			self					,   A_					,   A_    )  ->       Tuple:
         try:
              __UpperCamelCase    							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase    							=pl_module.model.num_parameters()

         __UpperCamelCase    							=count_trainable_parameters(A_    )
         # 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 _a					(			self					,   A_					,   A_    )  ->       List[str]:
         save_json(pl_module.metrics					,   pl_module.metrics_save_path    )
         return self._write_logs(A_					,   A_					,   'test'    )






    @rank_zero_only
    def _a					(			self					,   A_					,   A_    )  ->       Union[str, Any]:
         save_json(pl_module.metrics					,   pl_module.metrics_save_path    )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





