


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case					:		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",
}

_snake_case					:		int      =    AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=False     ):
							__snake_case			,      __snake_case		:      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     ):
							__snake_case		:      Optional[int]										=       {}

							__snake_case		:      Optional[int]										=       R".*sequential.(\d+).*"
							__snake_case		:      Dict										=       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:
																												__snake_case		:      Optional[Any]										=       key.replace(__lowerCamelCase							,     __lowerCamelCase     )

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

																					__snake_case		:      Optional[int]										=       key.replace(F'sequential.{sequential_layer}.'							,     F'layers.{int(__lowerCamelCase     )//3}.linear.'     )
														elif re.match(__lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Dict										=       int(re.match(__lowerCamelCase							,     __lowerCamelCase     ).group(1     )     )

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

																					__snake_case		:      List[Any]										=       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
																					__snake_case		:      Optional[Any]										=       value
																					__snake_case		:      Dict										=       mixed_qkv.size(0     ) // 3

																					__snake_case		:      int										=       mixed_qkv[:qkv_dim]
																					__snake_case		:      str										=       mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case		:      Union[str, Any]										=       mixed_qkv[qkv_dim * 2 :]

																					__snake_case		:      int										=       query_layer
																					__snake_case		:      Optional[Any]										=       key_layer
																					__snake_case		:      str										=       value_layer
														else:
																					__snake_case		:      Union[str, Any]										=       value

							return model_state_dict







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=False     ):
							__snake_case			,      __snake_case		:      Optional[int]										=       init_clap(__lowerCamelCase							,     enable_fusion=__lowerCamelCase     )

							clap_model.eval()
							__snake_case		:      List[Any]										=       clap_model.state_dict()
							__snake_case		:      Any										=       rename_state_dict(__lowerCamelCase     )

							__snake_case		:      str										=       ClapConfig()
							__snake_case		:      Any										=       enable_fusion
							__snake_case		:      Any										=       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__":
			_snake_case					:		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")
			_snake_case					:		int      =    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


_snake_case					:		Any      =    logging.get_logger(__name__)

_snake_case					:		Dict      =    {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "speech_to_text_2"
							__UpperCAmelCase   :							Dict					  =  ["past_key_values"]
							__UpperCAmelCase   :							str					  =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

							def __init__(							self	:  str   ,     lowerCamelCase	:  List[Any]=10000   ,     lowerCamelCase	:  Tuple=6   ,     lowerCamelCase	:  Optional[Any]=2048   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  str="relu"   ,     lowerCamelCase	:  Tuple=256   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  Any=0.0   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  List[Any]=1024   ,     **lowerCamelCase	:  List[str]   ,     )     ->   Any:
														__snake_case		:      Tuple										=       vocab_size
														__snake_case		:      int										=       d_model
														__snake_case		:      List[str]										=       decoder_ffn_dim
														__snake_case		:      Union[str, Any]										=       decoder_layers
														__snake_case		:      Dict										=       decoder_attention_heads
														__snake_case		:      Any										=       dropout
														__snake_case		:      List[str]										=       attention_dropout
														__snake_case		:      Optional[Any]										=       activation_dropout
														__snake_case		:      Union[str, Any]										=       activation_function
														__snake_case		:      Union[str, Any]										=       init_std
														__snake_case		:      Union[str, Any]										=       decoder_layerdrop
														__snake_case		:      Optional[int]										=       use_cache
														__snake_case		:      Optional[int]										=       decoder_layers
														__snake_case		:      str										=       scale_embedding  # scale factor will be sqrt(d_model) if True
														__snake_case		:      Dict										=       max_target_positions

														super().__init__(
														    pad_token_id=lowerCamelCase   ,     bos_token_id=lowerCamelCase   ,     eos_token_id=lowerCamelCase   ,     decoder_start_token_id=lowerCamelCase   ,     **lowerCamelCase   ,     )

