





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_A :   int  			=  Lock()



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Union[str, Any]:







			"""simple docstring"""
			global process_lock

			# we perform n swaps since after n swaps we know we are sorted
			# we *could* stop early if we are sorted already, but it takes as long to
			# find out we are sorted as it does to sort the list with this algorithm
			for i in range(0      ,  10					):
						if (i + position) % 2 == 0 and r_send is not None:
									# send your value to your right neighbor
									process_lock.acquire()
									r_send[1].send(UpperCAmelCase					)
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									lowerCamelCase__       :						Dict        =							rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									lowerCamelCase__       :						Any        =							min(UpperCAmelCase      ,  UpperCAmelCase					)
						elif (i + position) % 2 != 0 and l_send is not None:
									# send your value to your left neighbor
									process_lock.acquire()
									l_send[1].send(UpperCAmelCase					)
									process_lock.release()

									# receive your left neighbor's value
									process_lock.acquire()
									lowerCamelCase__       :						Optional[int]        =							lr_cv[0].recv()
									process_lock.release()

									# take the higher value since you are on the right
									lowerCamelCase__       :						Optional[Any]        =							max(UpperCAmelCase      ,  UpperCAmelCase					)
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(UpperCAmelCase					)



def 	_a						(   UpperCAmelCase					)    ->    str:







			"""simple docstring"""
			lowerCamelCase__       :						int        =							[]
			lowerCamelCase__       :						List[str]        =							[]
			# initialize the list of pipes where the values will be retrieved
			for _ in arr:
						result_pipe.append(Pipe()					)
			# creates the processes
			# the first and last process only have one neighbor so they are made outside
			# of the loop
			lowerCamelCase__       :						Optional[Any]        =							Pipe()
			lowerCamelCase__       :						Optional[int]        =							Pipe()
			process_array_.append(
			    Process(
			        target=UpperCAmelCase      ,  args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])      ,  )					)
			lowerCamelCase__       :						str        =							temp_rs
			lowerCamelCase__       :						str        =							temp_rr

			for i in range(1      ,  len(UpperCAmelCase					) - 1					):
						lowerCamelCase__       :						Optional[int]        =							Pipe()
						lowerCamelCase__       :						List[str]        =							Pipe()
						process_array_.append(
						    Process(
						        target=UpperCAmelCase      ,  args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])      ,  )					)
						lowerCamelCase__       :						Dict        =							temp_rs
						lowerCamelCase__       :						Any        =							temp_rr

			process_array_.append(
			    Process(
			        target=UpperCAmelCase      ,  args=(
			            len(UpperCAmelCase					) - 1,
			            arr[len(UpperCAmelCase					) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(UpperCAmelCase					) - 1],
			        )      ,  )					)

			# start the processes
			for p in process_array_:
						p.start()

			# wait for the processes to end and write their values to the list
			for p in range(0      ,  len(UpperCAmelCase					)					):
						lowerCamelCase__       :						Union[str, Any]        =							result_pipe[p][0].recv()
						process_array_[p].join()
			return arr



def 	_a						(   )    ->    Dict:







			"""simple docstring"""
			lowerCamelCase__       :						Tuple        =							list(range(10      ,  0      ,  -1					)					)
			print('''Initial List'''					)
			print(*UpperCAmelCase					)
			lowerCamelCase__       :						Dict        =							odd_even_transposition(UpperCAmelCase					)
			print('''Sorted List\n'''					)
			print(*UpperCAmelCase					)


if __name__ == "__main__":
							main()










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[Any]  			=  {
    '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 :   Any  			=  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							create_model(
       '''HTSAT-tiny'''      ,  '''roberta'''      ,  UpperCAmelCase      ,  precision='''fp32'''      ,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,  enable_fusion=UpperCAmelCase      ,  fusion_type='''aff_2d''' if enable_fusion else None      ,  )
   return model, model_cfg



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						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:
            lowerCamelCase__       :						List[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

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

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

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

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

         lowerCamelCase__       :						Optional[int]        =							query_layer
         lowerCamelCase__       :						str        =							key_layer
         lowerCamelCase__       :						List[str]        =							value_layer
      else:
         lowerCamelCase__       :						Tuple        =							value

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							ClapModel(UpperCAmelCase					)

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

   model.save_pretrained(UpperCAmelCase					)
   transformers_config.save_pretrained(UpperCAmelCase					)


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 :   Tuple  			=  parser.parse_args()

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



