




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		:				int									=     logging.get_logger(__name__)
UpperCAmelCase_		:				Any									=     {'vocab_file': 'spiece.model'}

UpperCAmelCase_		:				str									=     {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

UpperCAmelCase_		:				List[Any]									=     {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

UpperCAmelCase_		:				Dict									=     '▁'




class 		__A      (			UpperCamelCase_			):
						UpperCamelCase     						=					VOCAB_FILES_NAMES
						UpperCamelCase     						=					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase     						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

						def __init__(		self   :List[Any]    , __snake_case   :List[Any]    , __snake_case   :Any=True    , __snake_case   :str=True    , __snake_case   :str=False    , __snake_case   :Optional[int]="[CLS]"    , __snake_case   :str="[SEP]"    , __snake_case   :Optional[int]="<unk>"    , __snake_case   :str="[SEP]"    , __snake_case   :Optional[int]="<pad>"    , __snake_case   :Optional[Any]="[CLS]"    , __snake_case   :List[str]="[MASK]"    , __snake_case   :Optional[Dict[str, Any]] = None    , **__snake_case   :List[Any]    , ):
										'''simple docstring'''

										__magic_name__	:	Dict								=(
										    AddedToken(UpperCamelCase__    , lstrip=UpperCamelCase__    , rstrip=UpperCamelCase__    , normalized=UpperCamelCase__ )
										    if isinstance(UpperCamelCase__    , UpperCamelCase__ )
										    else mask_token
										)

										__magic_name__	:	str								={} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=UpperCamelCase__    , remove_space=UpperCamelCase__    , keep_accents=UpperCamelCase__    , bos_token=UpperCamelCase__    , eos_token=UpperCamelCase__    , unk_token=UpperCamelCase__    , sep_token=UpperCamelCase__    , pad_token=UpperCamelCase__    , cls_token=UpperCamelCase__    , mask_token=UpperCamelCase__    , sp_model_kwargs=self.sp_model_kwargs    , **UpperCamelCase__    , )

										__magic_name__	:	str								=do_lower_case
										__magic_name__	:	List[str]								=remove_space
										__magic_name__	:	Tuple								=keep_accents
										__magic_name__	:	Tuple								=vocab_file

										__magic_name__	:	List[str]								=spm.SentencePieceProcessor(**self.sp_model_kwargs )
										self.sp_model.Load(UpperCamelCase__ )

						@property
						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										return len(self.sp_model )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[str]								={self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
										vocab.update(self.added_tokens_encoder )
										return vocab

						def __getstate__(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.__dict__.copy()
										__magic_name__	:	Tuple								=None
										return state

						def __setstate__(		self   :List[Any]    , __snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=d

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

										__magic_name__	:	Union[str, Any]								=spm.SentencePieceProcessor(**self.sp_model_kwargs )
										self.sp_model.Load(self.vocab_file )

						def     A__						(		self   :Dict    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										if self.remove_space:
														__magic_name__	:	str								=''' '''.join(inputs.strip().split() )
										else:
														__magic_name__	:	Optional[int]								=inputs
										__magic_name__	:	List[str]								=outputs.replace("""``"""    , """\"""" ).replace("""\'\'"""    , """\"""" )

										if not self.keep_accents:
														__magic_name__	:	Any								=unicodedata.normalize("""NFKD"""    , UpperCamelCase__ )
														__magic_name__	:	int								=''''''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase__ )] )
										if self.do_lower_case:
														__magic_name__	:	Union[str, Any]								=outputs.lower()

										return outputs

						def     A__						(		self   :Any    , __snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.preprocess_text(UpperCamelCase__ )
										__magic_name__	:	Union[str, Any]								=self.sp_model.encode(UpperCamelCase__    , out_type=UpperCamelCase__ )
										__magic_name__	:	str								=[]
										for piece in pieces:
														if len(UpperCamelCase__ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
																		__magic_name__	:	Any								=self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__    , """""" ) )
																		if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0] ) == 1:
																										__magic_name__	:	Tuple								=cur_pieces[1:]
																						else:
																										__magic_name__	:	List[Any]								=cur_pieces[0][1:]
																		cur_pieces.append(piece[-1] )
																		new_pieces.extend(UpperCamelCase__ )
														else:
																		new_pieces.append(UpperCamelCase__ )

										return new_pieces

						def     A__						(		self   :Dict    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										return self.sp_model.PieceToId(UpperCamelCase__ )

						def     A__						(		self   :Union[str, Any]    , __snake_case   :Tuple ):
										'''simple docstring'''

										return self.sp_model.IdToPiece(UpperCamelCase__ )

						def     A__						(		self   :List[Any]    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=[]
										__magic_name__	:	List[str]								=''''''
										__magic_name__	:	str								=False
										for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																		if not prev_is_special:
																						out_string += " "
																		out_string += self.sp_model.decode(UpperCamelCase__ ) + token
																		__magic_name__	:	Union[str, Any]								=True
																		__magic_name__	:	List[str]								=[]
														else:
																		current_sub_tokens.append(UpperCamelCase__ )
																		__magic_name__	:	Tuple								=False
										out_string += self.sp_model.decode(UpperCamelCase__ )
										return out_string.strip()

						def     A__						(		self   :Union[str, Any]    , __snake_case   :List[int]    , __snake_case   :Optional[List[int]] = None ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=[self.sep_token_id]
										__magic_name__	:	int								=[self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

						def     A__						(		self   :Dict    , __snake_case   :List[int]    , __snake_case   :Optional[List[int]] = None    , __snake_case   :bool = False ):
										'''simple docstring'''

										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__    , token_ids_a=UpperCamelCase__    , already_has_special_tokens=UpperCamelCase__ )

										if token_ids_a is not None:
														return [1] + ([0] * len(UpperCamelCase__ )) + [1] + ([0] * len(UpperCamelCase__ )) + [1]
										return [1] + ([0] * len(UpperCamelCase__ )) + [1]

						def     A__						(		self   :Tuple    , __snake_case   :List[int]    , __snake_case   :Optional[List[int]] = None ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=[self.sep_token_id]
										__magic_name__	:	Optional[Any]								=[self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]


						def     A__						(		self   :Dict    , __snake_case   :str    , __snake_case   :Optional[str] = None ):
										'''simple docstring'''

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

										if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
														copyfile(self.vocab_file    , UpperCamelCase__ )
										elif not os.path.isfile(self.vocab_file ):
														with open(UpperCamelCase__    , """wb""" ) as fi:
																		__magic_name__	:	int								=self.sp_model.serialized_model_proto()
																		fi.write(UpperCamelCase__ )

										return (out_vocab_file,)










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[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",
}

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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	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   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	Union[str, 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:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

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

												__magic_name__	:	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
												__magic_name__	:	Dict								=value
												__magic_name__	:	List[str]								=mixed_qkv.size(0   ) // 3

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=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__":
							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_		:				Dict									=     parser.parse_args()

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




