

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A						:							int							=  {
    """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						:							int							=  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					create_model(
			    """HTSAT-tiny"""					,	"""roberta"""					,	SCREAMING_SNAKE_CASE					,	precision="""fp32"""					,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""					,	enable_fusion=SCREAMING_SNAKE_CASE					,	fusion_type="""aff_2d""" if enable_fusion else None					,	)
			return model, model_cfg
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{}

			SCREAMING_SNAKE_CASE           =					r""".*sequential.(\d+).*"""
			SCREAMING_SNAKE_CASE           =					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:
												SCREAMING_SNAKE_CASE           =					key.replace(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

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

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

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

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

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

									SCREAMING_SNAKE_CASE           =					query_layer
									SCREAMING_SNAKE_CASE           =					key_layer
									SCREAMING_SNAKE_CASE           =					value_layer
						else:
									SCREAMING_SNAKE_CASE           =					value

			return model_state_dict
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					init_clap(SCREAMING_SNAKE_CASE					,	enable_fusion=SCREAMING_SNAKE_CASE   )

			clap_model.eval()
			SCREAMING_SNAKE_CASE           =					clap_model.state_dict()
			SCREAMING_SNAKE_CASE           =					rename_state_dict(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					ClapConfig()
			SCREAMING_SNAKE_CASE           =					enable_fusion
			SCREAMING_SNAKE_CASE           =					ClapModel(SCREAMING_SNAKE_CASE   )

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

			model.save_pretrained(SCREAMING_SNAKE_CASE   )
			transformers_config.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[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						:							Union[str, Any]							=  parser.parse_args()

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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					position_embedding_type
										SCREAMING_SNAKE_CASE           =					use_cache






