






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


snake_case     					=		{
    """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""",
}

snake_case     					=		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")


def 	lowerCamelCase__      (  lowercase    ,  lowercase=False					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {}

  SCREAMING_SNAKE_CASE							:   Tuple						    =     R".*sequential.(\d+).*"
  SCREAMING_SNAKE_CASE							:   Tuple						    =     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							:   List[str]						    =     key.replace(lowercase    ,  lowercase					)

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

      SCREAMING_SNAKE_CASE							:   Tuple						    =     key.replace(F'''sequential.{sequential_layer}.'''    ,  F'''layers.{int(lowercase					)//3}.linear.'''					)
    elif re.match(lowercase    ,  lowercase					):
      SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     int(re.match(lowercase    ,  lowercase					).group(1					)					)

      # Because in CLAP they use `nn.Sequential`...
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     1 if projecton_layer == 0 else 2

      SCREAMING_SNAKE_CASE							:   Tuple						    =     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							:   List[Any]						    =     value
      SCREAMING_SNAKE_CASE							:   List[str]						    =     mixed_qkv.size(0					) // 3

      SCREAMING_SNAKE_CASE							:   str						    =     mixed_qkv[:qkv_dim]
      SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     mixed_qkv[qkv_dim : qkv_dim * 2]
      SCREAMING_SNAKE_CASE							:   Any						    =     mixed_qkv[qkv_dim * 2 :]

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

  return model_state_dict


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase    ,  lowercase=False					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     init_clap(lowercase    ,  enable_fusion=lowercase					)

  clap_model.eval()
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     clap_model.state_dict()
  SCREAMING_SNAKE_CASE							:   str						    =     rename_state_dict(lowercase					)

  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     ClapConfig()
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     enable_fusion
  SCREAMING_SNAKE_CASE							:   Dict						    =     ClapModel(lowercase					)

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

  model.save_pretrained(lowercase					)
  transformers_config.save_pretrained(lowercase					)


if __name__ == "__main__":
   snake_case     					=		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""")
   snake_case     					=		parser.parse_args()

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












import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_lowerCamelCase      :						int							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       set()
       A__ 					=       []

       def parse_line(lowercase_		):
              for line in fp:
                     if isinstance(lowercase_					,							lowercase_		):
                            A__ 					=       line.decode('''UTF-8'''		)
                     if "warnings summary (final)" in line:
                            continue
                     # This means we are outside the body of a warning
                     elif not line.startswith(''' '''		):
                            # process a single warning and move it to `selected_warnings`.
                            if len(lowercase_		) > 0:
                                   A__ 					=       '''\n'''.join(lowercase_		)
                                   # Only keep the warnings specified in `targets`
                                   if any(f""": {x}: """ in warning for x in targets		):
                                          selected_warnings.add(lowercase_		)
                                   buffer.clear()
                            continue
                     else:
                            A__ 					=       line.strip()
                            buffer.append(lowercase_		)

       if from_gh:
              for filename in os.listdir(lowercase_		):
                     A__ 					=       os.path.join(lowercase_					,							lowercase_		)
                     if not os.path.isdir(lowercase_		):
                            # read the file
                            if filename != "warnings.txt":
                                   continue
                            with open(lowercase_		) as fp:
                                   parse_line(lowercase_		)
       else:
              try:
                     with zipfile.ZipFile(lowercase_		) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(lowercase_		):
                                          # read the file
                                          if filename != "warnings.txt":
                                                 continue
                                          with z.open(lowercase_		) as fp:
                                                 parse_line(lowercase_		)
              except Exception:
                     logger.warning(
                         f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""		)

       return selected_warnings







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       set()

       A__ 					=       [os.path.join(lowercase_					,							lowercase_		) for p in os.listdir(lowercase_		) if (p.endswith('''.zip'''		) or from_gh)]
       for p in paths:
              selected_warnings.update(extract_warnings_from_single_artifact(lowercase_					,							lowercase_		)		)

       return selected_warnings


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						int							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
  parser.add_argument(
      """--output_dir""",
      type=str,
      required=True,
      help="""Where to store the downloaded artifacts and other result files.""",
  )
  parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
  # optional parameters
  parser.add_argument(
      """--targets""",
      default="""DeprecationWarning,UserWarning,FutureWarning""",
      type=list_str,
      help="""Comma-separated list of target warning(s) which we want to extract.""",
  )
  parser.add_argument(
      """--from_gh""",
      action="""store_true""",
      help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
  )

  _lowerCamelCase      :						List[Any]							=  parser.parse_args()

  _lowerCamelCase      :						List[str]							=  args.from_gh
  if from_gh:
    # The artifacts have to be downloaded using `actions/download-artifact@v3`
    pass
  else:
    os.makedirs(args.output_dir, exist_ok=True)

    # get download links
    _lowerCamelCase      :						Any							=  get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    # download artifacts
    for idx, (name, url) in enumerate(artifacts.items()):
      print(name)
      print(url)
      print("""=""" * 80)
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

    # extract warnings from artifacts
  _lowerCamelCase      :						Any							=  extract_warnings(args.output_dir, args.targets)
  _lowerCamelCase      :						Optional[Any]							=  sorted(selected_warnings)
  with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


