


'''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       _snake_case     (  A				,					A=False )		->					Union[str, Any]:
       lowerCAmelCase__     ,      lowerCAmelCase__      =			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       _snake_case     (  A )		->					List[str]:
       lowerCAmelCase__      =			{}

       lowerCAmelCase__      =			R'''.*sequential.(\d+).*'''
       lowerCAmelCase__      =			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__      =			key.replace(A				,					A )

              if re.match(A				,					A ):
                     # replace sequential layers with list
                     lowerCAmelCase__      =			re.match(A				,					A ).group(1 )

                     lowerCAmelCase__      =			key.replace(F"""sequential.{sequential_layer}."""				,					F"""layers.{int(A )//3}.linear.""" )
              elif re.match(A				,					A ):
                     lowerCAmelCase__      =			int(re.match(A				,					A ).group(1 ) )

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

                     lowerCAmelCase__      =			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__      =			value
                     lowerCAmelCase__      =			mixed_qkv.size(0 ) // 3

                     lowerCAmelCase__      =			mixed_qkv[:qkv_dim]
                     lowerCAmelCase__      =			mixed_qkv[qkv_dim : qkv_dim * 2]
                     lowerCAmelCase__      =			mixed_qkv[qkv_dim * 2 :]

                     lowerCAmelCase__      =			query_layer
                     lowerCAmelCase__      =			key_layer
                     lowerCAmelCase__      =			value_layer
              else:
                     lowerCAmelCase__      =			value

       return model_state_dict


def       _snake_case     (  A				,					A				,					A				,					A=False )		->					int:
       lowerCAmelCase__     ,      lowerCAmelCase__      =			init_clap(A				,					enable_fusion=A )

       clap_model.eval()
       lowerCAmelCase__      =			clap_model.state_dict()
       lowerCAmelCase__      =			rename_state_dict(A )

       lowerCAmelCase__      =			ClapConfig()
       lowerCAmelCase__      =			enable_fusion
       lowerCAmelCase__      =			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__":
  __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'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			0

							for character_count in character_freq_dict.values():
														if character_count % 2:
																					odd_char += 1
							if odd_char > 1:
														return False
							return True


def       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)
							print(
							    '''> can_string_be_rearranged_as_palindrome()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


if __name__ == "__main__":
		__UpperCAmelCase					       =							input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__UpperCAmelCase					       =							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")