






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        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Tuple=False   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						{}

						lowerCAmelCase__       =						R".*sequential.(\d+).*"
						lowerCAmelCase__       =						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__       =						key.replace(UpperCamelCase_    ,   UpperCamelCase_   )

												if re.match(UpperCamelCase_    ,   UpperCamelCase_   ):
																		# replace sequential layers with list
																		lowerCAmelCase__       =						re.match(UpperCamelCase_    ,   UpperCamelCase_   ).group(1   )

																		lowerCAmelCase__       =						key.replace(F"sequential.{sequential_layer}."    ,   F"layers.{int(UpperCamelCase_   )//3}.linear."   )
												elif re.match(UpperCamelCase_    ,   UpperCamelCase_   ):
																		lowerCAmelCase__       =						int(re.match(UpperCamelCase_    ,   UpperCamelCase_   ).group(1   )   )

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

																		lowerCAmelCase__       =						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__       =						value
																		lowerCAmelCase__       =						mixed_qkv.size(0   ) // 3

																		lowerCAmelCase__       =						mixed_qkv[:qkv_dim]
																		lowerCAmelCase__       =						mixed_qkv[qkv_dim : qkv_dim * 2]
																		lowerCAmelCase__       =						mixed_qkv[qkv_dim * 2 :]

																		lowerCAmelCase__       =						query_layer
																		lowerCAmelCase__       =						key_layer
																		lowerCAmelCase__       =						value_layer
												else:
																		lowerCAmelCase__       =						value

						return model_state_dict


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Union[str, Any]=False   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						init_clap(UpperCamelCase_    ,   enable_fusion=UpperCamelCase_   )

						clap_model.eval()
						lowerCAmelCase__       =						clap_model.state_dict()
						lowerCAmelCase__       =						rename_state_dict(UpperCamelCase_   )

						lowerCAmelCase__       =						ClapConfig()
						lowerCAmelCase__       =						enable_fusion
						lowerCAmelCase__       =						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__":
	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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =None
	a_         =BloomTokenizerFast
	a_         =BloomTokenizerFast
	a_         =True
	a_         =False
	a_         ="""tokenizer_file"""
	a_         ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						BloomTokenizerFast.from_pretrained("bigscience/tokenizer"       )
							tokenizer.save_pretrained(self.tmpdirname       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							kwargs.update(self.special_tokens_map       )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase__       =						[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

							lowerCAmelCase__       =						tokenizer.batch_encode_plus(__UpperCAmelCase       )["input_ids"]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.batch_decode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=6       )->					str:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			# tokenizer_r.pad_token = None # Hotfixing padding = None
																			# Simple input
																			lowerCAmelCase__       =						"This is a simple input"
																			lowerCAmelCase__       =						["This is a simple input 1", "This is a simple input 2"]
																			lowerCAmelCase__       =						("This is a simple input", "This is a pair")
																			lowerCAmelCase__       =						[
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			try:
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )

																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																			except ValueError:
																									self.fail("Bloom Tokenizer should be able to deal with padding"       )

																			lowerCAmelCase__       =						None  # Hotfixing padding = None
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						load_dataset("xnli"		,    "all_languages"		,    split="test"		,    streaming=__UpperCAmelCase       )

							lowerCAmelCase__       =						next(iter(__UpperCAmelCase       )       )["premise"]  # pick up one data
							lowerCAmelCase__       =						list(sample_data.values()       )

							lowerCAmelCase__       =						list(map(tokenizer.encode		,    __UpperCAmelCase       )       )
							lowerCAmelCase__       =						[tokenizer.decode(__UpperCAmelCase		,    clean_up_tokenization_spaces=__UpperCAmelCase       ) for x in output_tokens]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )






