






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  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Tuple=False   ) ->   List[str]:
						"""simple docstring"""







						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				Dict   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}

						lowerCAmelCase__       =						R".*sequential.(\d+).*"
						lowerCAmelCase__       =						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:
																								lowerCAmelCase__       =						key.replace(snake_case_    ,   snake_case_   )

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

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

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

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

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

																		lowerCAmelCase__       =						query_layer
																		lowerCAmelCase__       =						key_layer
																		lowerCAmelCase__       =						value_layer
												else:
																		lowerCAmelCase__       =						value

						return model_state_dict


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Union[str, Any]=False   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						init_clap(snake_case_    ,   enable_fusion=snake_case_   )

						clap_model.eval()
						lowerCAmelCase__       =						clap_model.state_dict()
						lowerCAmelCase__       =						rename_state_dict(snake_case_   )

						lowerCAmelCase__       =						ClapConfig()
						lowerCAmelCase__       =						enable_fusion
						lowerCAmelCase__       =						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__":
	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)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

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






