


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase						=		{
    '''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''',
}

lowerCamelCase						=		AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= 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 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= {}

		lowerCAmelCase__       :      Dict   					= R'''.*sequential.(\d+).*'''
		lowerCAmelCase__       :      Optional[Any]   					= 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:
								lowerCAmelCase__       :      int   					= key.replace(_a     ,       _a	)

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

						lowerCAmelCase__       :      Any   					= key.replace(f'sequential.{sequential_layer}.'     ,       f'layers.{int(_a	)//3}.linear.'	)
				elif re.match(_a     ,       _a	):
						lowerCAmelCase__       :      Tuple   					= int(re.match(_a     ,       _a	).group(1	)	)

						# Because in CLAP they use `nn.Sequential`...
						lowerCAmelCase__       :      List[str]   					= 1 if projecton_layer == 0 else 2

						lowerCAmelCase__       :      str   					= 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
						lowerCAmelCase__       :      Union[str, Any]   					= value
						lowerCAmelCase__       :      Dict   					= mixed_qkv.size(0	) // 3

						lowerCAmelCase__       :      int   					= mixed_qkv[:qkv_dim]
						lowerCAmelCase__       :      Dict   					= mixed_qkv[qkv_dim : qkv_dim * 2]
						lowerCAmelCase__       :      Optional[Any]   					= mixed_qkv[qkv_dim * 2 :]

						lowerCAmelCase__       :      Any   					= query_layer
						lowerCAmelCase__       :      List[Any]   					= key_layer
						lowerCAmelCase__       :      List[str]   					= value_layer
				else:
						lowerCAmelCase__       :      str   					= value

		return model_state_dict






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= init_clap(_a     ,       enable_fusion=_a	)

		clap_model.eval()
		lowerCAmelCase__       :      Tuple   					= clap_model.state_dict()
		lowerCAmelCase__       :      Optional[int]   					= rename_state_dict(_a	)

		lowerCAmelCase__       :      Union[str, Any]   					= ClapConfig()
		lowerCAmelCase__       :      Optional[Any]   					= enable_fusion
		lowerCAmelCase__       :      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__":
		lowerCamelCase						=		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''')
		lowerCamelCase						=		parser.parse_args()

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''xlm-roberta-xl'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		str=25_0880		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2560		, _SCREAMING_SNAKE_CASE						:		int=36		, _SCREAMING_SNAKE_CASE						:		Optional[int]=32		, _SCREAMING_SNAKE_CASE						:		Any=1_0240		, _SCREAMING_SNAKE_CASE						:		List[str]="gelu"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=514		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, _SCREAMING_SNAKE_CASE						:		Tuple=0.02		, _SCREAMING_SNAKE_CASE						:		Dict=1E-05		, _SCREAMING_SNAKE_CASE						:		Tuple=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Tuple=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="absolute"		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Any=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	str:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      int   					= num_hidden_layers
			lowerCAmelCase__       :      Dict   					= num_attention_heads
			lowerCAmelCase__       :      str   					= hidden_act
			lowerCAmelCase__       :      List[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      List[str]   					= max_position_embeddings
			lowerCAmelCase__       :      Any   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      Optional[int]   					= layer_norm_eps
			lowerCAmelCase__       :      Optional[int]   					= position_embedding_type
			lowerCAmelCase__       :      Any   					= use_cache
			lowerCAmelCase__       :      List[Any]   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      int   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[Any]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			    ]		)




