



'''simple docstring'''





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCamelCase											=		{
    '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',
}

_UpperCamelCase											=		AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=False			) -> List[Any]:
  __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					create_model(
      'HTSAT-tiny'			,'roberta'			,_lowerCAmelCase			,precision='fp32'			,device='cuda:0' if torch.cuda.is_available() else 'cpu'			,enable_fusion=_lowerCAmelCase			,fusion_type='aff_2d' if enable_fusion else None			,)
  return model, model_cfg

def 			a_	(				_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			List[Any]       			=					{}

  __lowerCamelCase			:			int       			=					r'.*sequential.(\d+).*'
  __lowerCamelCase			:			str       			=					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:
        __lowerCamelCase			:			Tuple       			=					key.replace(_lowerCAmelCase			,_lowerCAmelCase			)

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

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

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

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

      __lowerCamelCase			:			Union[str, Any]       			=					mixed_qkv[:qkv_dim]
      __lowerCamelCase			:			Any       			=					mixed_qkv[qkv_dim : qkv_dim * 2]
      __lowerCamelCase			:			str       			=					mixed_qkv[qkv_dim * 2 :]

      __lowerCamelCase			:			Optional[int]       			=					query_layer
      __lowerCamelCase			:			str       			=					key_layer
      __lowerCamelCase			:			Optional[Any]       			=					value_layer
    else:
      __lowerCamelCase			:			Dict       			=					value

  return model_state_dict

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=False			) -> Union[str, Any]:
  __lowerCamelCase			,__lowerCamelCase			:			int       			=					init_clap(_lowerCAmelCase			,enable_fusion=_lowerCAmelCase			)

  clap_model.eval()
  __lowerCamelCase			:			Optional[Any]       			=					clap_model.state_dict()
  __lowerCamelCase			:			Union[str, Any]       			=					rename_state_dict(_lowerCAmelCase			)

  __lowerCamelCase			:			int       			=					ClapConfig()
  __lowerCamelCase			:			str       			=					enable_fusion
  __lowerCamelCase			:			Optional[Any]       			=					ClapModel(_lowerCAmelCase			)

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

  model.save_pretrained(_lowerCAmelCase			)
  transformers_config.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
   _UpperCamelCase											=		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')
   _UpperCamelCase											=		parser.parse_args()

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











'''simple docstring'''





import random
from typing import Any

def 			a_	(				_lowerCAmelCase			) -> list[Any]:
  for _ in range(len(_lowerCAmelCase			)			):
    __lowerCamelCase			:			Optional[Any]       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			:			str       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					data[b], data[a]
  return data


if __name__ == "__main__":
   _UpperCamelCase											=		[0, 1, 2, 3, 4, 5, 6, 7]
   _UpperCamelCase											=		['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






