




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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]     ):


      '''simple docstring'''



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


lowerCamelCase				:		Union[str, Any]     =   logging.getLogger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       Dict     ):


      '''simple docstring'''



      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}'
      elif metric == "loss":
            lowerCamelCase_		     =    '{val_avg_loss:.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=lowercase	,							filename=lowercase	,							monitor=f"""val_{metric}"""	,							mode='max'	,							save_top_k=1	,							every_n_epochs=1	,							)

      return checkpoint_callback

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       List[str]     ):


      '''simple docstring'''



      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 				a__ (				self					:  Optional[Any]						,							A_					:  Union[str, Any]						,							A_					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    {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					:  Dict						,							A_					:  pl.Trainer						,							A_					:  pl.LightningModule						,							A_					:  str						,							A_					:  List[str]=True  )   ->	None:

       """simple docstring"""






       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=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
                   lowerCamelCase_		     =    metrics[key]
                   if isinstance(A_						,							torch.Tensor  ):
                         lowerCamelCase_		     =    val.item()
                   lowerCamelCase_		     =    f"""{key}: {val:.6f}\n"""
                   writer.write(A_  )

       if not save_generations:
             return

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

 @rank_zero_only
 def 				a__ (				self					:  Dict						,							A_					:  str						,							A_					:  Any  )   ->	int:

       """simple docstring"""






       try:
             lowerCamelCase_		     =    pl_module.model.model.num_parameters()
       except AttributeError:
             lowerCamelCase_		     =    pl_module.model.num_parameters()

       lowerCamelCase_		     =    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					:  Optional[Any]						,							A_					:  pl.Trainer						,							A_					:  pl.LightningModule  )   ->	Optional[int]:

       """simple docstring"""






       save_json(pl_module.metrics						,							pl_module.metrics_save_path  )
       return self._write_logs(A_						,							A_						,							'test'  )





 @rank_zero_only
 def 				a__ (				self					:  str						,							A_					:  pl.Trainer						,							A_					:  Dict  )   ->	Optional[Any]:

       """simple docstring"""






       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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



