






"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)


lowerCAmelCase_     =	'''▁'''

lowerCAmelCase_     =	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_     =	{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowerCAmelCase_     =	{
    '''google/reformer-crime-and-punishment''': 524288,
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						VOCAB_FILES_NAMES
				a			=						PRETRAINED_VOCAB_FILES_MAP
				a			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a			=						["input_ids", "attention_mask"]





				def __init__(      self							:							Optional[Any]      ,    _A							:							int      ,    _A							:							Any="</s>"      ,    _A							:							Any="<unk>"      ,    _A							:							Union[str, Any]=[]      ,    _A							:							Optional[Dict[str, Any]] = None      ,    **_A							:							List[Any]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=_A      ,    unk_token=_A      ,    additional_special_tokens=_A      ,    sp_model_kwargs=self.sp_model_kwargs      ,    **_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    vocab_file
											_SCREAMING_SNAKE_CASE		:						str			  =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(_A)





				@property
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											return self.sp_model.get_piece_size()





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    {self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab





				def __getstate__(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.__dict__.copy()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    None
											return state





				def __setstate__(      self							:							Dict      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    d

											# for backward compatibility
											if not hasattr(self      ,    """sp_model_kwargs"""):
																		_SCREAMING_SNAKE_CASE		:						int			  =    {}

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							str):



											"""simple docstring"""







											return self.sp_model.encode(_A      ,    out_type=_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Optional[int]):



											"""simple docstring"""







											return self.sp_model.piece_to_id(_A)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											if index < self.sp_model.get_piece_size():
																		_SCREAMING_SNAKE_CASE		:						str			  =    self.sp_model.IdToPiece(_A)
											return token





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_A) + token
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
																		else:
																									current_sub_tokens.append(_A)
											out_string += self.sp_model.decode(_A)
											return out_string.strip()





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							str      ,    _A							:							Optional[str] = None):



											"""simple docstring"""







											if not os.path.isdir(_A):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											_SCREAMING_SNAKE_CASE		:						Any			  =    os.path.join(
											    _A      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file      ,    _A)
											elif not os.path.isfile(self.vocab_file):
																		with open(_A      ,    """wb""") as fi:
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.sp_model.serialized_model_proto()
																									fi.write(_A)

											return (out_vocab_file,)



"""simple docstring"""





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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    create_model(
							    """HTSAT-tiny"""     ,	"""roberta"""     ,	__SCREAMING_SNAKE_CASE     ,	precision="""fp32"""     ,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""     ,	enable_fusion=__SCREAMING_SNAKE_CASE     ,	fusion_type="""aff_2d""" if enable_fusion else None     ,	)
							return model, model_cfg



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_SNAKE_CASE		:						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:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														if re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					# replace sequential layers with list
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

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

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

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

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

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


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)


