






"""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 							snake_case (  A__      ,A__=False		):
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   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 							snake_case (  A__		):
			UpperCAmelCase_	:					Optional[int]     	=   {}

			UpperCAmelCase_	:					Optional[Any]     	=   r".*sequential.(\d+).*"
			UpperCAmelCase_	:					int     	=   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:
												UpperCAmelCase_	:					Tuple     	=   key.replace(A__      ,A__		)

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

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

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

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

									UpperCAmelCase_	:					Union[str, Any]     	=   mixed_qkv[:qkv_dim]
									UpperCAmelCase_	:					Any     	=   mixed_qkv[qkv_dim : qkv_dim * 2]
									UpperCAmelCase_	:					str     	=   mixed_qkv[qkv_dim * 2 :]

									UpperCAmelCase_	:					Any     	=   query_layer
									UpperCAmelCase_	:					Union[str, Any]     	=   key_layer
									UpperCAmelCase_	:					List[Any]     	=   value_layer
						else:
									UpperCAmelCase_	:					List[Any]     	=   value

			return model_state_dict

def 							snake_case (  A__      ,A__      ,A__      ,A__=False		):
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   init_clap(A__      ,enable_fusion=A__		)

			clap_model.eval()
			UpperCAmelCase_	:					Tuple     	=   clap_model.state_dict()
			UpperCAmelCase_	:					Optional[int]     	=   rename_state_dict(A__		)

			UpperCAmelCase_	:					Optional[Any]     	=   ClapConfig()
			UpperCAmelCase_	:					str     	=   enable_fusion
			UpperCAmelCase_	:					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)











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [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   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   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_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



