
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class __snake_case						( __snake_case	):
    _a						:		Union[str, Any]=							"Wav2Vec2FeatureExtractor"
    _a						:		Any=							"AutoTokenizer"
    def __init__(    self						,snake_case						,snake_case ):



           '''simple docstring'''







           super().__init__(snake_case_						,snake_case_ )
           lowercase				:	Dict			      = self.feature_extractor
           lowercase				:	str			      = False
    @classmethod
    def  _SCREAMING_SNAKE_CASE			(    cls						,snake_case						,**snake_case ):



           '''simple docstring'''







           try:
                  return super().from_pretrained(snake_case_						,**snake_case_ )
           except OSError:
                  warnings.warn(
                      f"Loading a tokenizer inside {cls.__name__} from a config that does not"
                      """ include a `tokenizer_class` attribute is deprecated and will be """
                      """removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`"""
                      """ attribute to either your `config.json` or `tokenizer_config.json` """
                      """file to suppress this warning: """						,snake_case_						,)

                  lowercase				:	Union[str, Any]			      = WavaVecaFeatureExtractor.from_pretrained(snake_case_						,**snake_case_ )
                  lowercase				:	Any			      = WavaVecaCTCTokenizer.from_pretrained(snake_case_						,**snake_case_ )

                  return cls(feature_extractor=snake_case_						,tokenizer=snake_case_ )
    def __call__(    self						,*snake_case						,**snake_case ):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case_						,**snake_case_ )

           if "raw_speech" in kwargs:
                  warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
                  lowercase				:	str			      = kwargs.pop("""raw_speech""" )
           else:
                  lowercase				:	str			      = kwargs.pop("""audio"""						,snake_case_ )
           lowercase				:	int			      = kwargs.pop("""sampling_rate"""						,snake_case_ )
           lowercase				:	str			      = kwargs.pop("""text"""						,snake_case_ )
           if len(snake_case_ ) > 0:
                  lowercase				:	List[Any]			      = args[0]
                  lowercase				:	Union[str, Any]			      = args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )

           if audio is not None:
                  lowercase				:	str			      = self.feature_extractor(snake_case_						,*snake_case_						,sampling_rate=snake_case_						,**snake_case_ )
           if text is not None:
                  lowercase				:	Dict			      = self.tokenizer(snake_case_						,**snake_case_ )

           if text is None:
                  return inputs
           elif audio is None:
                  return encodings
           else:
                  lowercase				:	Dict			      = encodings["""input_ids"""]
                  return inputs
    def  _SCREAMING_SNAKE_CASE			(    self						,*snake_case						,**snake_case ):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor.pad(*snake_case_						,**snake_case_ )

           lowercase				:	Tuple			      = kwargs.pop("""input_features"""						,snake_case_ )
           lowercase				:	Union[str, Any]			      = kwargs.pop("""labels"""						,snake_case_ )
           if len(snake_case_ ) > 0:
                  lowercase				:	Optional[Any]			      = args[0]
                  lowercase				:	Dict			      = args[1:]

           if input_features is not None:
                  lowercase				:	str			      = self.feature_extractor.pad(snake_case_						,*snake_case_						,**snake_case_ )
           if labels is not None:
                  lowercase				:	Optional[Any]			      = self.tokenizer.pad(snake_case_						,**snake_case_ )

           if labels is None:
                  return input_features
           elif input_features is None:
                  return labels
           else:
                  lowercase				:	Any			      = labels["""input_ids"""]
                  return input_features
    def  _SCREAMING_SNAKE_CASE			(    self						,*snake_case						,**snake_case ):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case_						,**snake_case_ )
    def  _SCREAMING_SNAKE_CASE			(    self						,*snake_case						,**snake_case ):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case_						,**snake_case_ )
    @contextmanager
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           warnings.warn(
               """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
               """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
               """your audio inputs, or in a separate call.""" )
           lowercase				:	Tuple			      = True
           lowercase				:	str			      = self.tokenizer
           yield
           lowercase				:	Dict			      = self.feature_extractor
           lowercase				:	List[Any]			      = False







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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




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


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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{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.'				)

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




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







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         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=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

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




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							count_trainable_parameters(snake_case_							)
         # 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				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





