


'''simple docstring'''






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a_    :			List[str]      				=			{
    """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_    :			Union[str, Any]      				=			AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")






def 		_A (lowerCAmelCase__     :Tuple ,  lowerCAmelCase__     :Union[str, Any]=False						)      ->     List[Any]:




     '''simple docstring'''



     _a						=							create_model(
         'HTSAT-tiny' ,  'roberta' ,  A__ ,  precision='fp32' ,  device='cuda:0' if torch.cuda.is_available() else 'cpu' ,  enable_fusion=A__ ,  fusion_type='aff_2d' if enable_fusion else None ,  )
     return model, model_cfg






def 		_A (lowerCAmelCase__     :List[str]						)      ->     str:




     '''simple docstring'''



     _a						=							{}

     _a						=							R'.*sequential.(\d+).*'
     _a						=							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:
                    _a						=							key.replace(A__ ,  A__						)

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

               _a						=							key.replace(f'sequential.{sequential_layer}.' ,  f'layers.{int(A__						)//3}.linear.'						)
          elif re.match(A__ ,  A__						):
               _a						=							int(re.match(A__ ,  A__						).group(1						)						)

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

               _a						=							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
               _a						=							value
               _a						=							mixed_qkv.size(0						) // 3

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

               _a						=							query_layer
               _a						=							key_layer
               _a						=							value_layer
          else:
               _a						=							value

     return model_state_dict






def 		_A (lowerCAmelCase__     :str ,  lowerCAmelCase__     :str ,  lowerCAmelCase__     :Union[str, Any] ,  lowerCAmelCase__     :int=False						)      ->     Dict:




     '''simple docstring'''



     _a						=							init_clap(A__ ,  enable_fusion=A__						)

     clap_model.eval()
     _a						=							clap_model.state_dict()
     _a						=							rename_state_dict(A__						)

     _a						=							ClapConfig()
     _a						=							enable_fusion
     _a						=							ClapModel(A__						)

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

     model.save_pretrained(A__						)
     transformers_config.save_pretrained(A__						)


if __name__ == "__main__":
 a_    :			int      				=			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_    :			Any      				=			parser.parse_args()

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








import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
              else:
                     new_doc_list.append(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
              if len(A__				) > 1:
                     raise ValueError(
                         F'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'				)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

       # "overview" gets special treatment and is always first
       if len(A__				) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Tuple =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	Any =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[str] =				0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

       if diff:
              if overwrite:
                     a__						:	Optional[Any] =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Optional[int] =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	int =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[Any] =				0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
    lowercase      :       List[Any]									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)


