




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__				:      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",
}

lowercase__				:      Union[str, Any]	   = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=False)      ->					Dict:
					a  ,				a     =				create_model(
					    "HTSAT-tiny"		, "roberta"		, __UpperCamelCase		, precision="fp32"		, device="cuda:0" if torch.cuda.is_available() else "cpu"		, enable_fusion=__UpperCamelCase		, fusion_type="aff_2d" if enable_fusion else None		, )
					return model, model_cfg

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
					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(__UpperCamelCase		, __UpperCamelCase)

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

															a     =				key.replace(f'''sequential.{sequential_layer}.'''		, f'''layers.{int(__UpperCamelCase)//3}.linear.''')
										elif re.match(__UpperCamelCase		, __UpperCamelCase):
															a     =				int(re.match(__UpperCamelCase		, __UpperCamelCase).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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=False)      ->					Tuple:
					a  ,				a     =				init_clap(__UpperCamelCase		, enable_fusion=__UpperCamelCase)

					clap_model.eval()
					a     =				clap_model.state_dict()
					a     =				rename_state_dict(__UpperCamelCase)

					a     =				ClapConfig()
					a     =				enable_fusion
					a     =				ClapModel(__UpperCamelCase)

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

					model.save_pretrained(__UpperCamelCase)
					transformers_config.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
			lowercase__				:      List[str]	   = 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")
			lowercase__				:      int	   = parser.parse_args()

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







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

