






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


snake_case__      :	Dict       	=      logging.getLogger(__name__)






def     _snake_case    (__lowercase    ,      __lowercase):
   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=__lowercase    ,      filename=__lowercase    ,      monitor=f"""val_{metric}"""    ,      mode='max'    ,      save_top_k=3    ,      every_n_epochs=1    ,      )
   return checkpoint_callback






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


class 						_a       (  pl.Callback    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     {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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=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=_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
           UpperCamelCase_									=     metrics[key]
           if isinstance(_UpperCAmelCase		,		torch.Tensor ):
              UpperCamelCase_									=     val.item()
           UpperCamelCase_									=     f"""{key}: {val:.6f}\n"""
           writer.write(_UpperCAmelCase )

     if not save_generations:
        return

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




  @rank_zero_only
  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     try:
        UpperCamelCase_									=     pl_module.model.model.num_parameters()
     except AttributeError:
        UpperCamelCase_									=     pl_module.model.num_parameters()

     UpperCamelCase_									=     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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     save_json(pl_module.metrics		,		pl_module.metrics_save_path )
     return self._write_logs(_UpperCAmelCase		,		_UpperCAmelCase		,		'test' )




  @rank_zero_only
  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     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_sentencepiece_available,
    is_torch_available,
)


snake_case__      :	List[str]       	=      {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	List[str]       	=      ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	Any       	=      [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 snake_case__      :	Optional[int]       	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





