






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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




def      __A					(     _lowercase					,			_lowercase=False ):

					'''simple docstring'''







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




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= {}

					_A								= R'''.*sequential.(\d+).*'''
					_A								= 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								= key.replace(_lowercase					,			_lowercase )

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

															_A								= key.replace(f"""sequential.{sequential_layer}."""					,			f"""layers.{int(_lowercase )//3}.linear.""" )
										elif re.match(_lowercase					,			_lowercase ):
															_A								= int(re.match(_lowercase					,			_lowercase ).group(1 ) )

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

															_A								= 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								= value
															_A								= mixed_qkv.size(0 ) // 3

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

															_A								= query_layer
															_A								= key_layer
															_A								= value_layer
										else:
															_A								= value

					return model_state_dict




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=False ):

					'''simple docstring'''







					_A							,_A								= init_clap(_lowercase					,			enable_fusion=_lowercase )

					clap_model.eval()
					_A								= clap_model.state_dict()
					_A								= rename_state_dict(_lowercase )

					_A								= ClapConfig()
					_A								= enable_fusion
					_A								= ClapModel(_lowercase )

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

					model.save_pretrained(_lowercase )
					transformers_config.save_pretrained(_lowercase )


if __name__ == "__main__":
				__A      =				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      =				parser.parse_args()

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











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



