



"""simple docstring"""

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__    :  Optional[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__    :  Optional[int]							    =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')




def __lowercase			(							_a		,			_a=False    ):
    snake_case_,	snake_case_ :     int				=	create_model(
        '''HTSAT-tiny'''		,			'''roberta'''		,			_a		,			precision='''fp32'''		,			device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,			enable_fusion=_a		,			fusion_type='''aff_2d''' if enable_fusion else None		,			)
    return model, model_cfg




def __lowercase			(							_a    ):
    snake_case_ :     List[str]				=	{}

    snake_case_ :     List[Any]				=	r'''.*sequential.(\d+).*'''
    snake_case_ :     str				=	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_ :     List[str]				=	key.replace(_a		,			_a    )

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

            snake_case_ :     List[Any]				=	key.replace(f"sequential.{sequential_layer}."		,			f"layers.{int(_a    )//3}.linear."    )
        elif re.match(_a		,			_a    ):
            snake_case_ :     Optional[int]				=	int(re.match(_a		,			_a    ).group(1    )    )

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

            snake_case_ :     Optional[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_ :     Dict				=	value
            snake_case_ :     Tuple				=	mixed_qkv.size(0    ) // 3

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

            snake_case_ :     Optional[int]				=	query_layer
            snake_case_ :     int				=	key_layer
            snake_case_ :     Tuple				=	value_layer
        else:
            snake_case_ :     str				=	value

    return model_state_dict




def __lowercase			(							_a		,			_a		,			_a		,			_a=False    ):
    snake_case_,	snake_case_ :     List[Any]				=	init_clap(_a		,			enable_fusion=_a    )

    clap_model.eval()
    snake_case_ :     int				=	clap_model.state_dict()
    snake_case_ :     Dict				=	rename_state_dict(_a    )

    snake_case_ :     str				=	ClapConfig()
    snake_case_ :     List[str]				=	enable_fusion
    snake_case_ :     List[Any]				=	ClapModel(_a    )

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

    model.save_pretrained(_a    )
    transformers_config.save_pretrained(_a    )


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

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










"""simple docstring"""

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


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





