


'''simple docstring'''

import os
import re
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				: Optional[int]							    =				{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_UpperCAmelCase				: List[Any]							    =				{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


class       a__				( lowerCAmelCase__      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	VOCAB_FILES_NAMES
				__UpperCamelCase    :		List[str]	=	PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase    :		List[str]	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase    :		Tuple	=	["input_ids", "attention_mask"]
				__UpperCamelCase    :		List[int]	=	[]




				def __init__(self       ,			__lowercase       ,			__lowercase="<unk>"       ,			__lowercase="<s>"       ,			__lowercase="</s>"       ,			__lowercase="<pad>"       ,			__lowercase="[SEP]"       ,			__lowercase="[MASK]"       ,			__lowercase="[CLS]"       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else bos_token
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else eos_token
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else unk_token
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else pad_token
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else cls_token
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =				AddedToken(_lowerCamelCase       ,			lstrip=_lowerCamelCase       ,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase       ,			_lowerCamelCase		) else mask_token

										__lowerCAmelCase							       =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=_lowerCamelCase       ,			eos_token=_lowerCamelCase       ,			unk_token=_lowerCamelCase       ,			pad_token=_lowerCamelCase       ,			sep_token=_lowerCamelCase       ,			mask_token=_lowerCamelCase       ,			cls_token=_lowerCamelCase       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**_lowerCamelCase       ,			)

										__lowerCAmelCase							       =				vocab_file

										__lowerCAmelCase							       =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(_lowerCamelCase		)




				@property
				def 					_snake_case  (self		):
										return self.sp_model.get_piece_size()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
										vocab.update(self.added_tokens_encoder		)
										return vocab




				def __getstate__(self		):
										__lowerCAmelCase							       =				self.__dict__.copy()
										__lowerCAmelCase							       =				None
										return state




				def __setstate__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				d

										# for backward compatibility
										if not hasattr(self       ,			'''sp_model_kwargs'''		):
																__lowerCAmelCase							       =				{}

										__lowerCAmelCase							       =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
										self.sp_model.Load(self.vocab_file		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.sp_model.encode(_lowerCamelCase       ,			out_type=_lowerCamelCase		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.sp_model.piece_to_id(_lowerCamelCase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				self.sp_model.IdToPiece(_lowerCamelCase		)
										return token




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				''''''
										__lowerCAmelCase							       =				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(_lowerCamelCase		) + token
																						__lowerCAmelCase							       =				True
																						__lowerCAmelCase							       =				[]
																else:
																						current_sub_tokens.append(_lowerCamelCase		)
																						__lowerCAmelCase							       =				False
										out_string += self.sp_model.decode(_lowerCamelCase		)
										return out_string.strip()




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = False       ,			__lowercase = None       ,			__lowercase = True       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				kwargs.pop('''use_source_tokenizer'''       ,			_lowerCamelCase		)

										__lowerCAmelCase							       =				self.convert_ids_to_tokens(_lowerCamelCase       ,			skip_special_tokens=_lowerCamelCase		)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										for token in filtered_tokens:
																if skip_special_tokens and token in self.all_special_ids:
																						continue
																if token in self.added_tokens_encoder:
																						if current_sub_text:
																												sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase		)		)
																												__lowerCAmelCase							       =				[]
																						sub_texts.append(_lowerCamelCase		)
																else:
																						current_sub_text.append(_lowerCamelCase		)
										if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase		)		)

										# Mimic the behavior of the Rust tokenizer:
										# No space before [MASK] and [SEP]
										if spaces_between_special_tokens:
																__lowerCAmelCase							       =				re.sub(R''' (\[(MASK|SEP)\])'''       ,			R'''\1'''       ,			''' '''.join(_lowerCamelCase		)		)
										else:
																__lowerCAmelCase							       =				''''''.join(_lowerCamelCase		)

										__lowerCAmelCase							       =				(
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																__lowerCAmelCase							       =				self.clean_up_tokenization(_lowerCamelCase		)
																return clean_text
										else:
																return text




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):





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

										if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		) and os.path.isfile(self.vocab_file		):
																copyfile(self.vocab_file       ,			_lowerCamelCase		)
										elif not os.path.isfile(self.vocab_file		):
																with open(_lowerCamelCase       ,			'''wb'''		) as fi:
																						__lowerCAmelCase							       =				self.sp_model.serialized_model_proto()
																						fi.write(_lowerCamelCase		)

										return (out_vocab_file,)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):





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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = False		):





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

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):
										__lowerCAmelCase							       =				[self.sep_token_id]
										__lowerCAmelCase							       =				[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]





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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				{}

						__lowerCAmelCase							       =				r'''.*sequential.(\d+).*'''
						__lowerCAmelCase							       =				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							       =				key.replace(lowerCamelCase,				lowerCamelCase)

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

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

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

																		__lowerCAmelCase							       =				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							       =				value
																		__lowerCAmelCase							       =				mixed_qkv.size(0) // 3

																		__lowerCAmelCase							       =				mixed_qkv[:qkv_dim]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim : qkv_dim * 2]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim * 2 :]

																		__lowerCAmelCase							       =				query_layer
																		__lowerCAmelCase							       =				key_layer
																		__lowerCAmelCase							       =				value_layer
												else:
																		__lowerCAmelCase							       =				value

						return model_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				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				: 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""")
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()

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

