


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


snake_case    :   Tuple	    =					{
    '''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''',
}

snake_case    :   Dict	    =					AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		str=False						):




      """simple docstring"""





      a						:str										=	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     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Dict										=	{}

      a						:List[Any]										=	R'''.*sequential.(\d+).*'''
      a						:List[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:
                        a						:int										=	key.replace(UpperCAmelCase_			,  UpperCAmelCase_						)

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

                  a						:Any										=	key.replace(F'''sequential.{sequential_layer}.'''			,  F'''layers.{int(UpperCAmelCase_						)//3}.linear.'''						)
            elif re.match(UpperCAmelCase_			,  UpperCAmelCase_						):
                  a						:Tuple										=	int(re.match(UpperCAmelCase_			,  UpperCAmelCase_						).group(1						)						)

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

                  a						:int										=	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
                  a						:Optional[Any]										=	value
                  a						:Any										=	mixed_qkv.size(0						) // 3

                  a						:int										=	mixed_qkv[:qkv_dim]
                  a						:Any										=	mixed_qkv[qkv_dim : qkv_dim * 2]
                  a						:List[Any]										=	mixed_qkv[qkv_dim * 2 :]

                  a						:Union[str, Any]										=	query_layer
                  a						:int										=	key_layer
                  a						:Optional[Any]										=	value_layer
            else:
                  a						:Optional[int]										=	value

      return model_state_dict
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      a						:Tuple										=	init_clap(UpperCAmelCase_			,  enable_fusion=UpperCAmelCase_						)

      clap_model.eval()
      a						:Tuple										=	clap_model.state_dict()
      a						:List[Any]										=	rename_state_dict(UpperCAmelCase_						)

      a						:Any										=	ClapConfig()
      a						:Optional[int]										=	enable_fusion
      a						:List[Any]										=	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__":
     snake_case    :   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''')
     snake_case    :   Any	    =					parser.parse_args()

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




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	MgpstrTokenizer
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	{}
 SCREAMING_SNAKE_CASE__				=	False




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().setUp()

       # fmt: off
       a						:int										=	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       a						:List[str]										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )

       a						:Any										=	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['''vocab_file''']   )
       with open(self.vocab_file ,  '''w''' ,  encoding='''utf-8'''   ) as fp:
             fp.write(json.dumps(_lowerCamelCase   ) + '''\n'''   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       return MgpstrTokenizer.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	'''tester'''
       a						:Union[str, Any]										=	'''tester'''
       return input_text, output_text




 @unittest.skip('''MGP-STR always lower cases letters.'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.get_tokenizers(do_lower_case=_lowerCamelCase   )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   a						:Any										=	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}   )
                   a						:str										=	tokenizer.encode([special_token] ,  add_special_tokens=_lowerCamelCase   )
                   self.assertEqual(len(_lowerCamelCase   ) ,  1   )

                   a						:Tuple										=	tokenizer.decode(_lowerCamelCase ,  skip_special_tokens=_lowerCamelCase   )
                   self.assertTrue(special_token not in decoded   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   a							,    a						:Tuple										=	self.get_input_output_texts(_lowerCamelCase   )

                   a						:Tuple										=	tokenizer.tokenize(_lowerCamelCase   )
                   a						:int										=	tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
                   a						:Optional[int]										=	tokenizer.encode(_lowerCamelCase ,  add_special_tokens=_lowerCamelCase   )
                   self.assertListEqual(_lowerCamelCase ,  _lowerCamelCase   )

                   a						:Any										=	tokenizer.convert_ids_to_tokens(_lowerCamelCase   )
                   self.assertNotEqual(len(_lowerCamelCase   ) ,  0   )
                   a						:str										=	tokenizer.decode(_lowerCamelCase   )
                   self.assertIsInstance(_lowerCamelCase ,  _lowerCamelCase   )

                   self.assertEqual(text_a.replace(''' ''' ,  ''''''   ) ,  _lowerCamelCase   )




 @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass





