import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase_    :		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""",
}

lowerCamelCase_    :		str							=	AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase=False						):
					__a ,				__a		 =       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						):
					__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(__lowerCamelCase				,    __lowerCamelCase						)

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

															__a		 =       key.replace(f'''sequential.{sequential_layer}.'''				,    f'''layers.{int(__lowerCamelCase						)//3}.linear.'''						)
										elif re.match(__lowerCamelCase				,    __lowerCamelCase						):
															__a		 =       int(re.match(__lowerCamelCase				,    __lowerCamelCase						).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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=False						):
					__a ,				__a		 =       init_clap(__lowerCamelCase				,    enable_fusion=__lowerCamelCase						)

					clap_model.eval()
					__a		 =       clap_model.state_dict()
					__a		 =       rename_state_dict(__lowerCamelCase						)

					__a		 =       ClapConfig()
					__a		 =       enable_fusion
					__a		 =       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_    :		Optional[int]							=	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_    :		Optional[Any]							=	parser.parse_args()

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





from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




