
"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCamelCase_       :    str						      =			{
    '''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''',
}

UpperCamelCase_       :    Optional[Any]						      =			AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def 				A_  (__a					, __a=False					):

					'''simple docstring'''





					A_			,     A_								=    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 				A_  (__a					):

					'''simple docstring'''





					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(__a					, __a					)

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

															A_								=    key.replace(f'sequential.{sequential_layer}.'					, f'layers.{int(__a					)//3}.linear.'					)
										elif re.match(__a					, __a					):
															A_								=    int(re.match(__a					, __a					).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 				A_  (__a					, __a					, __a					, __a=False					):

					'''simple docstring'''





					A_			,     A_								=    init_clap(__a					, enable_fusion=__a					)

					clap_model.eval()
					A_								=    clap_model.state_dict()
					A_								=    rename_state_dict(__a					)

					A_								=    ClapConfig()
					A_								=    enable_fusion
					A_								=    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__":
		UpperCamelCase_       :    Optional[int]						      =			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''')
		UpperCamelCase_       :    List[str]						      =			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


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




