


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__    (		snake_case__					:							Tuple     ):
  _UpperCAmelCase		:							Any							=     filter(lambda snake_case__     : p.requires_grad						,   model.parameters()     )
  _UpperCAmelCase		:							Optional[Any]							=     sum([np.prod(p.size()     ) for p in model_parameters]     )
  return params


SCREAMING_SNAKE_CASE__					:     int       =     logging.getLogger(__name__)




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							Optional[int]     ):
  if metric == "rouge2":
    _UpperCAmelCase		:							str							=     """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    _UpperCAmelCase		:							int							=     """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    _UpperCAmelCase		:							int							=     """{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		:							Dict							=     ModelCheckpoint(
      dirpath=snake_case__						,   filename=snake_case__						,   monitor=f'''val_{metric}'''						,   mode="""max"""						,   save_top_k=3						,   every_n_epochs=1						,   )
  return checkpoint_callback




def    a__    (		snake_case__					:							Dict						,   snake_case__					:							Tuple     ):
  return EarlyStopping(
      monitor=f'''val_{metric}'''						,   mode="""min""" if """loss""" in metric else """max"""						,   patience=snake_case__						,   verbose=snake_case__						,   )





class 		_SCREAMING_SNAKE_CASE     (						pl.Callback     ):


     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Tuple							=     {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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_=True    ):
       logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
       _UpperCAmelCase		:							List[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
       _UpperCAmelCase		:							str							=     Path(pl_module.hparams.output_dir    )
       if type_path == "test":
         _UpperCAmelCase		:							List[str]							=     od / """test_results.txt"""
         _UpperCAmelCase		:							List[Any]							=     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		:							str							=     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
         _UpperCAmelCase		:							Any							=     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		:							Union[str, Any]							=     metrics[key]
           if isinstance(A_							,					torch.Tensor    ):
             _UpperCAmelCase		:							str							=     val.item()
           _UpperCAmelCase		:							Optional[Any]							=     F'''{key}: {val:.6f}\n'''
           writer.write(A_    )

       if not save_generations:
         return

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


     @rank_zero_only
     def        __snake_case(  self							,					A_							,					A_    ):
       try:
         _UpperCAmelCase		:							Dict							=     pl_module.model.model.num_parameters()
       except AttributeError:
         _UpperCAmelCase		:							str							=     pl_module.model.num_parameters()

       _UpperCAmelCase		:							Optional[Any]							=     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        __snake_case(  self							,					A_							,					A_    ):
       save_json(pl_module.metrics							,					pl_module.metrics_save_path    )
       return self._write_logs(A_							,					A_							,					"""test"""    )




     @rank_zero_only
     def        __snake_case(  self							,					A_							,					A_    ):
       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


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     '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

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





