


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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




def    a   (							A__      ,    A__=False							)	-> List[str]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Dict						     =		{}

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		r'''.*sequential.(\d+).*'''
      SCREAMING_SNAKE_CASE__       :  Any						     =		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:
                        SCREAMING_SNAKE_CASE__       :  Dict						     =		key.replace(A__      ,    A__							)

            if re.match(A__      ,    A__							):
                  # replace sequential layers with list
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		re.match(A__      ,    A__							).group(1							)

                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		key.replace(f"""sequential.{sequential_layer}."""      ,    f"""layers.{int(A__							)//3}.linear."""							)
            elif re.match(A__      ,    A__							):
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		int(re.match(A__      ,    A__							).group(1							)							)

                  # Because in CLAP they use `nn.Sequential`...
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		1 if projecton_layer == 0 else 2

                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		value
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		mixed_qkv.size(0							) // 3

                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		mixed_qkv[:qkv_dim]
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		mixed_qkv[qkv_dim : qkv_dim * 2]
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		mixed_qkv[qkv_dim * 2 :]

                  SCREAMING_SNAKE_CASE__       :  str						     =		query_layer
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		key_layer
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		value_layer
            else:
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		value

      return model_state_dict




def    a   (							A__      ,    A__      ,    A__      ,    A__=False							)	-> List[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		init_clap(A__      ,    enable_fusion=A__							)

      clap_model.eval()
      SCREAMING_SNAKE_CASE__       :  int						     =		clap_model.state_dict()
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		rename_state_dict(A__							)

      SCREAMING_SNAKE_CASE__       :  str						     =		ClapConfig()
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		enable_fusion
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		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_ :List[Any]  							=			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)








"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


A__ :       Tuple				= [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  comments[0] if len(_UpperCamelCase    ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''    )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''    )
      issue.remove_from_labels('''stale'''    )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''    )
      issue.add_to_labels('''stale'''    )


if __name__ == "__main__":
  main()
