




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 lowerCamelCase__       (      _lowercase			,						_lowercase=False				):

  '''simple docstring'''


  UpperCAmelCase_      :							Dict       =      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'''


  UpperCAmelCase_      :							Dict       =      {}

  UpperCAmelCase_      :							Union[str, Any]       =      r'''.*sequential.(\d+).*'''
  UpperCAmelCase_      :							List[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:
        UpperCAmelCase_      :							str       =      key.replace(_lowercase			,						_lowercase				)

    if re.match(_lowercase			,						_lowercase				):
      # replace sequential layers with list
      UpperCAmelCase_      :							Tuple       =      re.match(_lowercase			,						_lowercase				).group(1				)

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

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

      UpperCAmelCase_      :							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
      UpperCAmelCase_      :							Union[str, Any]       =      value
      UpperCAmelCase_      :							Union[str, Any]       =      mixed_qkv.size(0				) // 3

      UpperCAmelCase_      :							int       =      mixed_qkv[:qkv_dim]
      UpperCAmelCase_      :							List[Any]       =      mixed_qkv[qkv_dim : qkv_dim * 2]
      UpperCAmelCase_      :							str       =      mixed_qkv[qkv_dim * 2 :]

      UpperCAmelCase_      :							Dict       =      query_layer
      UpperCAmelCase_      :							Optional[Any]       =      key_layer
      UpperCAmelCase_      :							Dict       =      value_layer
    else:
      UpperCAmelCase_      :							List[Any]       =      value

  return model_state_dict







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

  '''simple docstring'''


  UpperCAmelCase_      :							int       =      init_clap(_lowercase			,						enable_fusion=_lowercase				)

  clap_model.eval()
  UpperCAmelCase_      :							str       =      clap_model.state_dict()
  UpperCAmelCase_      :							Union[str, Any]       =      rename_state_dict(_lowercase				)

  UpperCAmelCase_      :							List[str]       =      ClapConfig()
  UpperCAmelCase_      :							Tuple       =      enable_fusion
  UpperCAmelCase_      :							Union[str, Any]       =      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__":
     __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)





from __future__ import annotations

from fractions import Fraction







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      []
     UpperCAmelCase_      :							Tuple       =      11
     UpperCAmelCase_      :							int       =      int('''1''' + '''0''' * digit_len				)
     for num in range(_lowercase			,						_lowercase				):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(_lowercase			,						_lowercase				):
                         solutions.append(f'''{num}/{den}'''				)
               den += 1
          num += 1
          UpperCAmelCase_      :							Any       =      10
     return solutions







def lowerCamelCase__       (      _lowercase = 2				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      1.0
     for fraction in fraction_list(_lowercase				):
          UpperCAmelCase_      :							Optional[Any]       =      Fraction(_lowercase				)
          result *= frac.denominator / frac.numerator
     return int(_lowercase				)


if __name__ == "__main__":
     print(solution())