




"""simple docstring"""
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a_				:    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''',
}

a_				:    Any										=     AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    str=False     )  ->     Tuple:
			__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				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 						UpperCAmelCase				(			A__:    List[str]     )  ->     List[str]:
			__lowerCamelCase					:	Union[str, Any]	       =				{}

			__lowerCamelCase					:	int	       =				r'.*sequential.(\d+).*'
			__lowerCamelCase					:	List[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:
												__lowerCamelCase					:	str	       =				key.replace(A__							,				A__     )

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

									__lowerCamelCase					:	Dict	       =				key.replace(f'''sequential.{sequential_layer}.'''							,				f'''layers.{int(A__     )//3}.linear.'''     )
						elif re.match(A__							,				A__     ):
									__lowerCamelCase					:	List[str]	       =				int(re.match(A__							,				A__     ).group(1     )     )

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

									__lowerCamelCase					:	Tuple	       =				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					:	Optional[int]	       =				value
									__lowerCamelCase					:	Optional[int]	       =				mixed_qkv.size(0     ) // 3

									__lowerCamelCase					:	Tuple	       =				mixed_qkv[:qkv_dim]
									__lowerCamelCase					:	List[Any]	       =				mixed_qkv[qkv_dim : qkv_dim * 2]
									__lowerCamelCase					:	Any	       =				mixed_qkv[qkv_dim * 2 :]

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

			return model_state_dict





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    int							,				A__:    List[Any]							,				A__:    List[str]=False     )  ->     str:
			__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				init_clap(A__							,				enable_fusion=A__     )

			clap_model.eval()
			__lowerCamelCase					:	Any	       =				clap_model.state_dict()
			__lowerCamelCase					:	Tuple	       =				rename_state_dict(A__     )

			__lowerCamelCase					:	Optional[int]	       =				ClapConfig()
			__lowerCamelCase					:	Dict	       =				enable_fusion
			__lowerCamelCase					:	int	       =				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__":
					a_				:    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''')
					a_				:    Union[str, Any]										=     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_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

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

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	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     snake_case_				(    self						,	__a						,	__a = None     ):
								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(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[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     ):
											copyfile(self.vocab_file						,	__a     )

								return (out_vocab_file,)



