
'''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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: int=False							):



						"""simple docstring"""





						lowercase_	:     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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





						lowercase_	:     Dict													=       {}

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

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

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

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

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

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

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

						return model_state_dict
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Tuple=False							):



						"""simple docstring"""





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

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

						lowercase_	:     Tuple													=       ClapConfig()
						lowercase_	:     str													=       enable_fusion
						lowercase_	:     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)





'''simple docstring'''




def 		lowerCAmelCase_				(   snake_case__ = 10						):




    '''simple docstring'''



    if not isinstance(snake_case__		,				snake_case__						) or n < 0:
        raise ValueError('''Invalid input'''						)
    A       :   List[str]					=						10**n
    A       :   Tuple					=						2_8433 * (pow(2		,				783_0457		,				snake_case__						)) + 1
    return str(number % modulus						)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(f'''{solution(10) = }''')




