"""simple docstring"""







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     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[str]									=			filter(lambda __SCREAMING_SNAKE_CASE   : p.requires_grad  ,    model.parameters()   )
     lowercase_			:       Optional[Any]									=			sum([np.prod(p.size()   ) for p in model_parameters]   )
     return params


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Dict   ):
     if metric == "rouge2":
          lowercase_			:       int									=			'{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_			:       Any									=			'{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          lowercase_			:       Optional[Any]									=			'{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.'   )

     lowercase_			:       int									=			ModelCheckpoint(
         dirpath=__SCREAMING_SNAKE_CASE  ,    filename=__SCREAMING_SNAKE_CASE  ,    monitor=F'''val_{metric}'''  ,    mode='max'  ,    save_top_k=3  ,    every_n_epochs=1  ,    )
     return checkpoint_callback



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     return EarlyStopping(
         monitor=F'''val_{metric}'''  ,    mode='min' if 'loss' in metric else 'max'  ,    patience=__SCREAMING_SNAKE_CASE  ,    verbose=__SCREAMING_SNAKE_CASE  ,    )







class  UpperCamelCase	(    pl.Callback						):
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[str]									=			{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:


        '''simple docstring'''


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

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_			:       int									=			'\n'.join(metrics['preds']    )
             generations_file.open('w+'    ).write(__UpperCamelCase    )
   @rank_zero_only
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        try:
             lowercase_			:       Optional[Any]									=			pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_			:       Optional[Any]									=			pl_module.model.num_parameters()

        lowercase_			:       Optional[int]									=			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    )			->   Any:


        '''simple docstring'''


        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    )			->   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")


"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_			:       Optional[Any]									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_			:       Dict									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_			:       Dict									=			downstream_dict['objective.W']
     return model



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[str]									=			hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'   ):
          lowercase_			:       List[Any]									=			convert_classification(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForAudioFrameClassification'   ):
          lowercase_			:       List[str]									=			convert_diarization(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForXVector'   ):
          lowercase_			:       Optional[Any]									=			convert_xvector(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     else:
          raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''   )

     if hf_config.use_weighted_layer_sum:
          lowercase_			:       Dict									=			checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=argparse.ArgumentParser()
       parser.add_argument(
           "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
       )
       parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
       parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

