



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

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A:   Optional[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''',
}

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

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		int=False						):
 UpperCAmelCase	:					int	=	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						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	{}

 UpperCAmelCase	:					List[Any]	=	r'''.*sequential.(\d+).*'''
 UpperCAmelCase	:					str	=	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	:					List[str]	=	key.replace(_a			,		_a						)

  if re.match(_a			,		_a						):
   # replace sequential layers with list
   UpperCAmelCase	:					Tuple	=	re.match(_a			,		_a						).group(1						)

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

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

   UpperCAmelCase	:					Optional[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
   UpperCAmelCase	:					Dict	=	value
   UpperCAmelCase	:					Tuple	=	mixed_qkv.size(0						) // 3

   UpperCAmelCase	:					List[Any]	=	mixed_qkv[:qkv_dim]
   UpperCAmelCase	:					int	=	mixed_qkv[qkv_dim : qkv_dim * 2]
   UpperCAmelCase	:					Union[str, Any]	=	mixed_qkv[qkv_dim * 2 :]

   UpperCAmelCase	:					Optional[int]	=	query_layer
   UpperCAmelCase	:					int	=	key_layer
   UpperCAmelCase	:					Tuple	=	value_layer
  else:
   UpperCAmelCase	:					str	=	value

 return model_state_dict

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Tuple=False						):
 UpperCAmelCase	:					List[Any]	=	init_clap(_a			,		enable_fusion=_a						)

 clap_model.eval()
 UpperCAmelCase	:					int	=	clap_model.state_dict()
 UpperCAmelCase	:					Dict	=	rename_state_dict(_a						)

 UpperCAmelCase	:					str	=	ClapConfig()
 UpperCAmelCase	:					List[str]	=	enable_fusion
 UpperCAmelCase	:					List[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__":
      A:   Union[str, 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")
      A:   List[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_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     lowercase__    :  int							    =	None


lowercase__    :  Any							    =	logging.get_logger(__name__)


lowercase__    :  List[str]							    =	'''▁'''

lowercase__    :  Optional[int]							    =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__    :  str							    =	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

lowercase__    :  List[Any]							    =	{
    '''google/pegasus-xsum''': 5_12,
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[str]							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			List[str]							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Any							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase   :			Tuple							=		PegasusTokenizer
 _lowerCAmelCase   :			str							=		["""input_ids""", """attention_mask"""]

 def __init__(     self		:       Any  ,     lowercase_		:       Optional[Any]=None  ,     lowercase_		:       int=None  ,     lowercase_		:       Tuple="<pad>"  ,     lowercase_		:       int="</s>"  ,     lowercase_		:       Tuple="<unk>"  ,     lowercase_		:       str="<mask_2>"  ,     lowercase_		:       Optional[Any]="<mask_1>"  ,     lowercase_		:       str=None  ,     lowercase_		:       List[str]=103  ,     **lowercase_		:       List[Any]  ,     ):
     snake_case_ :     Dict				=	offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_  ,     lowercase_				):
             raise TypeError(
                 f"additional_special_tokens should be of type {type(lowercase_				)}, but is"
                 f" {type(lowercase_				)}"				)

         snake_case_ :     str				=	(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f"<unk_{i}>" for i in range(len(lowercase_				)  ,     self.offset - 1				)
         ]

         if len(set(lowercase_				)				) != len(lowercase_				):
             raise ValueError(
                 '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                 f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."				)
         snake_case_ :     Union[str, Any]				=	additional_special_tokens_extended
     else:
         snake_case_ :     Dict				=	[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f"<unk_{i}>" for i in range(2  ,     self.offset				)]

     super().__init__(
         lowercase_  ,     tokenizer_file=lowercase_  ,     pad_token=lowercase_  ,     eos_token=lowercase_  ,     unk_token=lowercase_  ,     mask_token=lowercase_  ,     mask_token_sent=lowercase_  ,     offset=lowercase_  ,     additional_special_tokens=lowercase_  ,     **lowercase_  ,     )
     snake_case_ :     List[Any]				=	vocab_file
     snake_case_ :     List[Any]				=	False if not self.vocab_file else True

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Union[str, Any]				):
     snake_case_ :     Any				=	set(self.all_special_ids				)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens				) + 3				)				):
         raise ValueError(
             '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
             f" {len(self.additional_special_tokens				)} additional_special_tokens, but got {all_special_ids}"				)

     return [1 if x in all_special_ids else 0 for x in seq]

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       List  ,     lowercase_		:       Optional[List] = None  ,     lowercase_		:       bool = False				):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_				)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_				) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a				) + [1]

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       str=None				):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]






 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[str] = 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(lowercase_				):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
         return
     snake_case_ :     Dict				=	os.path.join(
         lowercase_  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				):
         copyfile(self.vocab_file  ,     lowercase_				)

     return (out_vocab_file,)





