


'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       a__				( UpperCamelCase_							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Dict	=	BarthezTokenizer
				__UpperCamelCase    :		int	=	BarthezTokenizerFast
				__UpperCamelCase    :		Union[str, Any]	=	True
				__UpperCamelCase    :		int	=	True




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

										__lowerCAmelCase							       =				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''		)
										tokenizer.save_pretrained(self.tmpdirname		)
										tokenizer.save_pretrained(self.tmpdirname       ,			legacy_format=UpperCamelCase__		)
										__lowerCAmelCase							       =				tokenizer




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''<pad>'''
										__lowerCAmelCase							       =				1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__		)       ,			UpperCamelCase__		)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__		)       ,			UpperCamelCase__		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]       ,			'''<s>'''		)
										self.assertEqual(vocab_keys[1]       ,			'''<pad>'''		)
										self.assertEqual(vocab_keys[-1]       ,			'''<mask>'''		)
										self.assertEqual(len(UpperCamelCase__		)       ,			10_11_22		)




				def 					_snake_case  (self		):
										self.assertEqual(self.get_tokenizer().vocab_size       ,			10_11_22		)




				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										__lowerCAmelCase							       =				[0, 57, 30_18, 7_03_07, 91, 2]

										__lowerCAmelCase							       =				self.tokenizer(
										    UpperCamelCase__       ,			max_length=len(UpperCamelCase__		)       ,			padding=UpperCamelCase__       ,			truncation=UpperCamelCase__       ,			return_tensors='''pt'''		)
										self.assertIsInstance(UpperCamelCase__       ,			UpperCamelCase__		)

										self.assertEqual((2, 6)       ,			batch.input_ids.shape		)
										self.assertEqual((2, 6)       ,			batch.attention_mask.shape		)
										__lowerCAmelCase							       =				batch.input_ids.tolist()[0]
										self.assertListEqual(UpperCamelCase__       ,			UpperCamelCase__		)




				def 					_snake_case  (self		):





										if not self.test_rust_tokenizer:
																return

										__lowerCAmelCase							       =				self.get_tokenizer()
										__lowerCAmelCase							       =				self.get_rust_tokenizer()

										__lowerCAmelCase							       =				'''I was born in 92000, and this is falsé.'''

										__lowerCAmelCase							       =				tokenizer.tokenize(UpperCamelCase__		)
										__lowerCAmelCase							       =				rust_tokenizer.tokenize(UpperCamelCase__		)
										self.assertListEqual(UpperCamelCase__       ,			UpperCamelCase__		)

										__lowerCAmelCase							       =				tokenizer.encode(UpperCamelCase__       ,			add_special_tokens=UpperCamelCase__		)
										__lowerCAmelCase							       =				rust_tokenizer.encode(UpperCamelCase__       ,			add_special_tokens=UpperCamelCase__		)
										self.assertListEqual(UpperCamelCase__       ,			UpperCamelCase__		)

										__lowerCAmelCase							       =				self.get_rust_tokenizer()
										__lowerCAmelCase							       =				tokenizer.encode(UpperCamelCase__		)
										__lowerCAmelCase							       =				rust_tokenizer.encode(UpperCamelCase__		)
										self.assertListEqual(UpperCamelCase__       ,			UpperCamelCase__		)




				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# moussaKam/mbarthez is a french model. So we also use french texts.
										__lowerCAmelCase							       =				[
										    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
										    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
										    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
										    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
										    '''telles que la traduction et la synthèse de texte.''',
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCamelCase__       ,			model_name='''moussaKam/mbarthez'''       ,			revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''       ,			sequences=UpperCamelCase__       ,			)





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCAmelCase				: str							    =				{
    """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""",
}

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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				create_model(
						    '''HTSAT-tiny''',				'''roberta''',				lowerCamelCase,				precision='''fp32''',				device='''cuda:0''' if torch.cuda.is_available() else '''cpu''',				enable_fusion=lowerCamelCase,				fusion_type='''aff_2d''' if enable_fusion else None,				)
						return model, model_cfg

def 							__magic_name__(						lowerCamelCase):
						__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(lowerCamelCase,				lowerCamelCase)

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

																		__lowerCAmelCase							       =				key.replace(F"""sequential.{sequential_layer}.""",				F"""layers.{int(lowerCamelCase)//3}.linear.""")
												elif re.match(lowerCamelCase,				lowerCamelCase):
																		__lowerCAmelCase							       =				int(re.match(lowerCamelCase,				lowerCamelCase).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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				ClapModel(lowerCamelCase)

						# ignore the spectrogram embedding layer
						model.load_state_dict(lowerCamelCase,				strict=lowerCamelCase)

						model.save_pretrained(lowerCamelCase)
						transformers_config.save_pretrained(lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: 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""")
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()

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

