



'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase									=   {
    '''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''',
}

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a       ,					_a     :      Union[str, Any]        =							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      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		_a     :      Optional[int]        =							{}

		_a     :      Tuple        =							r'.*sequential.(\d+).*'
		_a     :      int        =							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     :      Union[str, Any]        =							key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

				if re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						# replace sequential layers with list
						_a     :      List[str]        =							re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )

						_a     :      Optional[Any]        =							key.replace(f"""sequential.{sequential_layer}."""						,			f"""layers.{int(lowerCAmelCase_  )//3}.linear."""  )
				elif re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						_a     :      str        =							int(re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )  )

						# Because in CLAP they use `nn.Sequential`...
						_a     :      Optional[Any]        =							1 if projecton_layer == 0 else 2

						_a     :      int        =							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     :      str        =							value
						_a     :      List[str]        =							mixed_qkv.size(0  ) // 3

						_a     :      str        =							mixed_qkv[:qkv_dim]
						_a     :      int        =							mixed_qkv[qkv_dim : qkv_dim * 2]
						_a     :      Any        =							mixed_qkv[qkv_dim * 2 :]

						_a     :      List[Any]        =							query_layer
						_a     :      Union[str, Any]        =							key_layer
						_a     :      Tuple        =							value_layer
				else:
						_a     :      Dict        =							value

		return model_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a       ,					_a     :      Optional[Any]        =							init_clap(lowerCAmelCase_						,			enable_fusion=lowerCAmelCase_  )

		clap_model.eval()
		_a     :      Tuple        =							clap_model.state_dict()
		_a     :      Optional[int]        =							rename_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							ClapConfig()
		_a     :      Tuple        =							enable_fusion
		_a     :      int        =							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__":
							__lowerCAmelCase									=   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''')
							__lowerCAmelCase									=   parser.parse_args()

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




