

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowerCAmelCase							:  Optional[int]   	=						logging.get_logger(__name__)


lowerCAmelCase							:  Optional[int]   	=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase							:  int   	=						{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

lowerCAmelCase							:  List[str]   	=						{
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}







@lru_cache()
def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       (
						    list(range(ord("!"    )				,	ord("~"    ) + 1    )    ) + list(range(ord("¡"    )				,	ord("¬"    ) + 1    )    ) + list(range(ord("®"    )				,	ord("ÿ"    ) + 1    )    )
						)
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       bs[:]
						SCREAMING_SNAKE_CASE_: int  		=       0
						for b in range(2**8    ):
												if b not in bs:
																		bs.append(_UpperCAmelCase    )
																		cs.append(2**8 + n    )
																		n += 1
						SCREAMING_SNAKE_CASE_: Any  		=       [chr(_UpperCAmelCase    ) for n in cs]
						return dict(zip(_UpperCAmelCase				,	_UpperCAmelCase    )    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       set()
						SCREAMING_SNAKE_CASE_: int  		=       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)    )
												SCREAMING_SNAKE_CASE_: Tuple  		=       char
						return pairs



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Tuple										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							Any										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							Union[str, Any]										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase   :							str										=     ['''input_ids''', '''attention_mask''']



			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Optional[int]="replace"   ,						lowerCAmelCase__  :     List[Any]="<s>"   ,						lowerCAmelCase__  :     List[Any]="</s>"   ,						lowerCAmelCase__  :     str="</s>"   ,						lowerCAmelCase__  :     Dict="<s>"   ,						lowerCAmelCase__  :     int="<unk>"   ,						lowerCAmelCase__  :     Union[str, Any]="<pad>"   ,						lowerCAmelCase__  :     str="<mask>"   ,						lowerCAmelCase__  :     Optional[int]=False   ,						**lowerCAmelCase__  :     Optional[int]   ,						):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_: str  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_: Any  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_: Any  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else unk_token
									SCREAMING_SNAKE_CASE_: str  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else pad_token

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

									super().__init__(
									    errors=lowerCAmelCase__   ,						bos_token=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						sep_token=lowerCAmelCase__   ,						cls_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						mask_token=lowerCAmelCase__   ,						add_prefix_space=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									with open(lowerCAmelCase__   ,						encoding="utf-8") as vocab_handle:
															SCREAMING_SNAKE_CASE_: str  		=       json.load(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       errors  # how to handle errors in decoding
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       bytes_to_unicode()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       {v: k for k, v in self.byte_encoder.items()}
									with open(lowerCAmelCase__   ,						encoding="utf-8") as merges_handle:
															SCREAMING_SNAKE_CASE_: Any  		=       merges_handle.read().split("\n")[1:-1]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [tuple(merge.split()) for merge in bpe_merges]
									SCREAMING_SNAKE_CASE_: List[str]  		=       dict(zip(lowerCAmelCase__   ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									SCREAMING_SNAKE_CASE_: List[str]  		=       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return len(self.encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return dict(self.encoder   ,						**self.added_tokens_encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_: str  		=       tuple(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       get_pairs(lowerCAmelCase__)

									if not pairs:
															return token

									while True:
															SCREAMING_SNAKE_CASE_: str  		=       min(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__   ,						float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       bigram
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
															SCREAMING_SNAKE_CASE_: Any  		=       0
															while i < len(lowerCAmelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_: Optional[Any]  		=       word.index(lowerCAmelCase__   ,						lowerCAmelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       j

																					if word[i] == first and i < len(lowerCAmelCase__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																					else:
																											new_word.append(word[i])
																											i += 1
															SCREAMING_SNAKE_CASE_: Tuple  		=       tuple(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: int  		=       new_word
															if len(lowerCAmelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_: int  		=       get_pairs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       " ".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       word
									return word



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       []
									for token in re.findall(self.pat   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       "".join(
															    self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__).split(" "))
									return bpe_tokens



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     List[Any]):
									return self.encoder.get(lowerCAmelCase__   ,						self.encoder.get(self.unk_token))



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.decoder.get(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       "".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"   ,						errors=self.errors)
									return text



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_: List[Any]  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

									with open(lowerCAmelCase__   ,						"w"   ,						encoding="utf-8") as f:
															f.write(json.dumps(self.encoder   ,						indent=2   ,						sort_keys=lowerCAmelCase__   ,						ensure_ascii=lowerCAmelCase__) + "\n")

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       0
									with open(lowerCAmelCase__   ,						"w"   ,						encoding="utf-8") as writer:
															writer.write("#version: 0.2\n")
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,						key=lambda lowerCAmelCase__: kv[1]):
																					if index != token_index:
																											logger.warning(
																											    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																											    " Please check that the tokenizer is not corrupted!")
																											SCREAMING_SNAKE_CASE_: str  		=       token_index
																					writer.write(" ".join(lowerCAmelCase__) + "\n")
																					index += 1

									return vocab_file, merge_file



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [self.cls_token_id]
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = 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, 1] + ([0] * len(lowerCAmelCase__)) + [1]



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Any  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: str  		=       [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 + sep + token_ids_a + sep) * [0]




			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						**lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       kwargs.pop("add_prefix_space"   ,						self.add_prefix_space)
									if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__) > 0 and not text[0].isspace()):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       " " + text
									return (text, kwargs)









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, Any]   	=						{
    """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							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

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

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

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

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

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

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						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							:  int   	=						parser.parse_args()

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






