


"""simple docstring"""




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A			=						{
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

_A			=						AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Any=False    ):
     __UpperCamelCase							,				__UpperCamelCase   							=create_model(
         'HTSAT-tiny'      ,					'roberta'      ,					SCREAMING_SNAKE_CASE__      ,					precision='fp32'      ,					device='cuda:0' if torch.cuda.is_available() else 'cpu'      ,					enable_fusion=SCREAMING_SNAKE_CASE__      ,					fusion_type='aff_2d' if enable_fusion else None      ,					)
     return model, model_cfg

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							={}

     __UpperCamelCase   							=r'.*sequential.(\d+).*'
     __UpperCamelCase   							=r'.*_projection.(\d+).*'

     for key, value in state_dict.items():
          # check if any key needs to be modified
          for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
               if key_to_modify in key:
                    __UpperCamelCase   							=key.replace(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

          if re.match(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               # replace sequential layers with list
               __UpperCamelCase   							=re.match(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ).group(1    )

               __UpperCamelCase   							=key.replace(F'sequential.{sequential_layer}.'      ,					F'layers.{int(SCREAMING_SNAKE_CASE__    )//3}.linear.'    )
          elif re.match(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=int(re.match(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ).group(1    )    )

               # Because in CLAP they use `nn.Sequential`...
               __UpperCamelCase   							=1 if projecton_layer == 0 else 2

               __UpperCamelCase   							=key.replace(F'_projection.{projecton_layer}.'      ,					F'_projection.linear{transformers_projection_layer}.'    )

          if "audio" and "qkv" in key:
               # split qkv into query key and value
               __UpperCamelCase   							=value
               __UpperCamelCase   							=mixed_qkv.size(0    ) // 3

               __UpperCamelCase   							=mixed_qkv[:qkv_dim]
               __UpperCamelCase   							=mixed_qkv[qkv_dim : qkv_dim * 2]
               __UpperCamelCase   							=mixed_qkv[qkv_dim * 2 :]

               __UpperCamelCase   							=query_layer
               __UpperCamelCase   							=key_layer
               __UpperCamelCase   							=value_layer
          else:
               __UpperCamelCase   							=value

     return model_state_dict

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=False    ):
     __UpperCamelCase							,				__UpperCamelCase   							=init_clap(SCREAMING_SNAKE_CASE__      ,					enable_fusion=SCREAMING_SNAKE_CASE__    )

     clap_model.eval()
     __UpperCamelCase   							=clap_model.state_dict()
     __UpperCamelCase   							=rename_state_dict(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=ClapConfig()
     __UpperCamelCase   							=enable_fusion
     __UpperCamelCase   							=ClapModel(SCREAMING_SNAKE_CASE__    )

     # ignore the spectrogram embedding layer
     model.load_state_dict(SCREAMING_SNAKE_CASE__      ,					strict=SCREAMING_SNAKE_CASE__    )

     model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     transformers_config.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
   _A			=						parser.parse_args()

   convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)









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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     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}'
     elif metric == "loss":
          __UpperCamelCase   							='{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.'    )

     __UpperCamelCase   							=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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     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		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={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 ,       A_ ,       A_ ,       A_ ,       A_=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=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
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

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






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=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 ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	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")





