


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 os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Features    ):
     __UpperCamelCase   							=np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE__      : FeatureType    ) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) and feature.dtype == "binary":
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

     _visit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None if batch_size is np.inf else batch_size






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = None ,       **A_ ,       )  ->	Dict:
         super().__init__(
             A_ ,       split=A_ ,       features=A_ ,       cache_dir=A_ ,       keep_in_memory=A_ ,       streaming=A_ ,       num_proc=A_ ,       **A_ ,       )
         __UpperCamelCase   							=path_or_paths if isinstance(A_ ,       A_   ) else {self.split: path_or_paths}
         __UpperCamelCase   							=_PACKAGED_DATASETS_MODULES['parquet'][1]
         __UpperCamelCase   							=Parquet(
             cache_dir=A_ ,       data_files=A_ ,       features=A_ ,       hash=A_ ,       **A_ ,       )






    def _a (      self   )  ->	List[Any]:
         # Build iterable dataset
         if self.streaming:
              __UpperCamelCase   							=self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None

              self.builder.download_and_prepare(
                  download_config=A_ ,       download_mode=A_ ,       verification_mode=A_ ,       base_path=A_ ,       num_proc=self.num_proc ,       )
              __UpperCamelCase   							=self.builder.as_dataset(
                  split=self.split ,       verification_mode=A_ ,       in_memory=self.keep_in_memory   )
         return dataset






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=path_or_buf
         __UpperCamelCase   							=batch_size or get_writer_batch_size(dataset.features   )
         __UpperCamelCase   							=parquet_writer_kwargs






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf ,       (str, bytes, os.PathLike)   ):
              with open(self.path_or_buf ,       'wb+'   ) as buffer:
                   __UpperCamelCase   							=self._write(file_obj=A_ ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         else:
              __UpperCamelCase   							=self._write(file_obj=self.path_or_buf ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         return written






    def _a (      self ,       A_ ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=0
         __UpperCamelCase   							=parquet_writer_kwargs.pop('path_or_buf' ,       A_   )
         __UpperCamelCase   							=self.dataset.features.arrow_schema

         __UpperCamelCase   							=pq.ParquetWriter(A_ ,       schema=A_ ,       **A_   )

         for offset in logging.tqdm(
             range(0 ,       len(self.dataset   ) ,       A_   ) ,       unit='ba' ,       disable=not logging.is_progress_bar_enabled() ,       desc='Creating parquet from Arrow format' ,       ):
              __UpperCamelCase   							=query_table(
                  table=self.dataset._data ,       key=slice(A_ ,       offset + batch_size   ) ,       indices=self.dataset._indices if self.dataset._indices is not None else None ,       )
              writer.write_table(A_   )
              written += batch.nbytes
         writer.close()
         return written





