





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								lowerCamelCase	 ="laion/clap-htsat-unfused"
								lowerCamelCase	 =tempfile.mkdtemp()


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return RobertaTokenizer.from_pretrained(self.checkpoint   , **UpperCAmelCase_							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return ClapFeatureExtractor.from_pretrained(self.checkpoint   , **UpperCAmelCase_							)


				def _snake_case       (		self							):
								shutil.rmtree(self.tmpdirname							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =self.get_feature_extractor()

								lowerCamelCase	 =ClapProcessor(tokenizer=UpperCAmelCase_   , feature_extractor=UpperCAmelCase_							)

								processor.save_pretrained(self.tmpdirname							)
								lowerCamelCase	 =ClapProcessor.from_pretrained(self.tmpdirname							)

								self.assertEqual(processor.tokenizer.get_vocab()   , tokenizer.get_vocab()							)
								self.assertIsInstance(processor.tokenizer   , UpperCAmelCase_							)

								self.assertEqual(processor.feature_extractor.to_json_string()   , feature_extractor.to_json_string()							)
								self.assertIsInstance(processor.feature_extractor   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =ClapProcessor(tokenizer=self.get_tokenizer()   , feature_extractor=self.get_feature_extractor()							)
								processor.save_pretrained(self.tmpdirname							)

								lowerCamelCase	 =self.get_tokenizer(bos_token="""(BOS)"""   , eos_token="""(EOS)"""							)
								lowerCamelCase	 =self.get_feature_extractor(do_normalize=UpperCAmelCase_   , padding_value=1.0							)

								lowerCamelCase	 =ClapProcessor.from_pretrained(
								    self.tmpdirname   , bos_token="""(BOS)"""   , eos_token="""(EOS)"""   , do_normalize=UpperCAmelCase_   , padding_value=1.0							)

								self.assertEqual(processor.tokenizer.get_vocab()   , tokenizer_add_kwargs.get_vocab()							)
								self.assertIsInstance(processor.tokenizer   , UpperCAmelCase_							)

								self.assertEqual(processor.feature_extractor.to_json_string()   , feature_extractor_add_kwargs.to_json_string()							)
								self.assertIsInstance(processor.feature_extractor   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_feature_extractor()
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =ClapProcessor(tokenizer=UpperCAmelCase_   , feature_extractor=UpperCAmelCase_							)

								lowerCamelCase	 =floats_list((3, 1000)							)

								lowerCamelCase	 =feature_extractor(UpperCAmelCase_   , return_tensors="""np"""							)
								lowerCamelCase	 =processor(audios=UpperCAmelCase_   , return_tensors="""np"""							)

								for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()   , input_processor[key].sum()   , delta=1E-2							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_feature_extractor()
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =ClapProcessor(tokenizer=UpperCAmelCase_   , feature_extractor=UpperCAmelCase_							)

								lowerCamelCase	 ="This is a test string"

								lowerCamelCase	 =processor(text=UpperCAmelCase_							)

								lowerCamelCase	 =tokenizer(UpperCAmelCase_							)

								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]   , encoded_processor[key]							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_feature_extractor()
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =ClapProcessor(tokenizer=UpperCAmelCase_   , feature_extractor=UpperCAmelCase_							)

								lowerCamelCase	 =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase	 =processor.batch_decode(UpperCAmelCase_							)
								lowerCamelCase	 =tokenizer.batch_decode(UpperCAmelCase_							)

								self.assertListEqual(UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_feature_extractor()
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =ClapProcessor(tokenizer=UpperCAmelCase_   , feature_extractor=UpperCAmelCase_							)

								self.assertListEqual(
								    processor.model_input_names[2:]   , feature_extractor.model_input_names   , msg="""`processor` and `feature_extractor` model input names do not match"""   , )












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





