






def __A    (      __lowerCAmelCase	)->  bool:





						"""simple docstring"""



						_UpperCAmelCase									=			[int(__lowerCAmelCase	) for i in ip_va_address.split('.'	) if i.isdigit()]
						return len(__lowerCAmelCase	) == 4 and all(0 <= int(__lowerCAmelCase	) <= 254 for octet in octets	)


if __name__ == "__main__":
		_a      	=  input().strip()
		_a      	=  '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
		print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')


'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase			:				Union[str, 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',
}

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   Tuple					=						create_model(
        '''HTSAT-tiny'''		,				'''roberta'''		,				snake_case__		,				precision='''fp32'''		,				device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,				enable_fusion=snake_case__		,				fusion_type='''aff_2d''' if enable_fusion else None		,				)
    return model, model_cfg







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{}

    A       :   str					=						R'''.*sequential.(\d+).*'''
    A       :   Union[str, 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:
                A       :   Any					=						key.replace(snake_case__		,				snake_case__						)

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

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

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

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

            A       :   Union[str, Any]					=						mixed_qkv[:qkv_dim]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim : qkv_dim * 2]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim * 2 :]

            A       :   Tuple					=						query_layer
            A       :   Union[str, Any]					=						key_layer
            A       :   Optional[int]					=						value_layer
        else:
            A       :   Dict					=						value

    return model_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   int					=						init_clap(snake_case__		,				enable_fusion=snake_case__						)

    clap_model.eval()
    A       :   str					=						clap_model.state_dict()
    A       :   Union[str, Any]					=						rename_state_dict(snake_case__						)

    A       :   Tuple					=						ClapConfig()
    A       :   str					=						enable_fusion
    A       :   str					=						ClapModel(snake_case__						)

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

    model.save_pretrained(snake_case__						)
    transformers_config.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			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')
      lowercase			:				Tuple    		=			parser.parse_args()

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




