






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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






def   a			(						lowerCamelCase_		,   lowerCamelCase_=False				):






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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			{}

				lowercase__    =			r'''.*sequential.(\d+).*'''
				lowercase__    =			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__    =			key.replace(lowerCamelCase_		,   lowerCamelCase_				)

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

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

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

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

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

												lowercase__    =			query_layer
												lowercase__    =			key_layer
												lowercase__    =			value_layer
								else:
												lowercase__    =			value

				return model_state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=False				):






				'''simple docstring'''
				lowercase__ ,						lowercase__    =			init_clap(lowerCamelCase_		,   enable_fusion=lowerCamelCase_				)

				clap_model.eval()
				lowercase__    =			clap_model.state_dict()
				lowercase__    =			rename_state_dict(lowerCamelCase_				)

				lowercase__    =			ClapConfig()
				lowercase__    =			enable_fusion
				lowercase__    =			ClapModel(lowerCamelCase_				)

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

				model.save_pretrained(lowerCamelCase_				)
				transformers_config.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							A__	:			Any			   = 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)












import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




