





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 import save_json



def _a							(      lowerCamelCase:  str					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							filter(lambda lowerCamelCase					: p.requires_grad   ,							model.parameters()					)
      __A			  =							sum([np.prod(p.size()					) for p in model_parameters]					)
      return params


snake_case__   :				Optional[int]						=	logging.getLogger(__name__)





class A_   (     pl.Callback  ):




   def _lowerCAmelCase (self      :Optional[Any]       ,  _UpperCamelCase      :Dict       ,  _UpperCamelCase      :List[str]					)->   str:
         __A			  =							{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      :Union[str, Any]       ,  _UpperCamelCase      :pl.Trainer       ,  _UpperCamelCase      :pl.LightningModule       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :str=True					)->   None:
         logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
         __A			  =							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			  =							Path(pl_module.hparams.output_dir					)
         if type_path == "test":
               __A			  =							od / '''test_results.txt'''
               __A			  =							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			  =							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
               __A			  =							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
                     __A			  =							metrics[key]
                     if isinstance(_UpperCamelCase       ,  torch.Tensor					):
                           __A			  =							val.item()
                     __A			  =							f"""{key}: {val:.6f}\n"""
                     writer.write(_UpperCamelCase					)

         if not save_generations:
               return

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




   @rank_zero_only
   def _lowerCAmelCase (self      :Dict       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :List[Any]					)->   Any:
         try:
               __A			  =							pl_module.model.model.num_parameters()
         except AttributeError:
               __A			  =							pl_module.model.num_parameters()

         __A			  =							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      :Optional[int]       ,  _UpperCamelCase      :pl.Trainer       ,  _UpperCamelCase      :pl.LightningModule					)->   Dict:
         save_json(pl_module.metrics       ,  pl_module.metrics_save_path					)
         return self._write_logs(_UpperCamelCase       ,  _UpperCamelCase       ,  '''test'''					)






   @rank_zero_only
   def _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :pl.Trainer       ,  _UpperCamelCase      :str					)->   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")





def _a							(      lowerCamelCase:  Dict   ,							lowerCamelCase:  str   ,							lowerCamelCase:  List[Any]=1   ,							lowerCamelCase:  List[str]=False					)      ->		str:







      '''simple docstring'''






      if metric == "rouge2":
            __A			  =							'''{val_avg_rouge2:.4f}-{step_count}'''
      elif metric == "bleu":
            __A			  =							'''{val_avg_bleu:.4f}-{step_count}'''
      elif metric == "loss":
            __A			  =							'''{val_avg_loss:.4f}-{step_count}'''
      else:
            raise NotImplementedError(
                F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
                ''' this function.'''					)

      __A			  =							ModelCheckpoint(
          dirpath=lowerCamelCase   ,							filename=lowerCamelCase   ,							monitor=F"""val_{metric}"""   ,							mode='''min''' if '''loss''' in metric else '''max'''   ,							save_top_k=lowerCamelCase   ,							)
      return checkpoint_callback







def _a							(      lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Union[str, Any]					)      ->		Any:







      '''simple docstring'''






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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





