

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a__												=						{
    '''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__												=						AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def   __UpperCAmelCase					(      __a :     Any						,__a :     List[str]=False      )     ->	List[Any]:

					"""simple docstring"""

					_a ,   _a      :       List[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 :     str      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Any   =						{}

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

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

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

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

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

															_a      :       Tuple   =						mixed_qkv[:qkv_dim]
															_a      :       Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
															_a      :       Dict   =						mixed_qkv[qkv_dim * 2 :]

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

					return model_state_dict



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Optional[int]						,__a :     Dict						,__a :     Dict=False      )     ->	Any:

					"""simple docstring"""

					_a ,   _a      :       Optional[Any]   =						init_clap(__a						,enable_fusion=__a      )

					clap_model.eval()
					_a      :       Tuple   =						clap_model.state_dict()
					_a      :       Union[str, Any]   =						rename_state_dict(__a      )

					_a      :       Union[str, Any]   =						ClapConfig()
					_a      :       Dict   =						enable_fusion
					_a      :       Dict   =						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__												=						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__												=						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 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[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      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_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(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

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

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


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

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


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



