
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 			UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '      )
									SCREAMING_SNAKE_CASE           =     dict(zip(lowercase__		,     range(len(lowercase__      )      )      )      )

									SCREAMING_SNAKE_CASE           =     {
									    'pad_token': '<pad>',
									    'unk_token': '<unk>',
									    'bos_token': '<s>',
									    'eos_token': '</s>',
									}
									SCREAMING_SNAKE_CASE           =     {
									    'feature_size': 1,
									    'padding_value': 0.0,
									    'sampling_rate': 16000,
									    'return_attention_mask': False,
									    'do_normalize': True,
									}

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

									with open(self.feature_extraction_file		,     'w'		,     encoding='utf-8'      ) as fp:
														fp.write(json.dumps(lowercase__      ) + '\n'      )


				def 							A (    self		,     **lowercase__      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.add_kwargs_tokens_map.copy()
									kwargs.update(lowercase__      )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **lowercase__      )


				def 							A (    self		,     **lowercase__      )   ->   Tuple:
									"""simple docstring"""







									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,     **lowercase__      )


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname      )


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()
									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									processor.save_pretrained(self.tmpdirname      )
									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor.from_pretrained(self.tmpdirname      )

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

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


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									SCREAMING_SNAKE_CASE           =     self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )
									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor(do_normalize=lowercase__		,     padding_value=1.0      )

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor.from_pretrained(
									    self.tmpdirname		,     bos_token='(BOS)'		,     eos_token='(EOS)'		,     do_normalize=lowercase__		,     padding_value=1.0      )

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

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


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     floats_list((3, 1000)      )

									SCREAMING_SNAKE_CASE           =     feature_extractor(lowercase__		,     return_tensors='np'      )
									SCREAMING_SNAKE_CASE           =     processor(lowercase__		,     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 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'This is a test string'

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									SCREAMING_SNAKE_CASE           =     tokenizer(lowercase__      )

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


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

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

									SCREAMING_SNAKE_CASE           =     processor.batch_decode(lowercase__      )
									SCREAMING_SNAKE_CASE           =     tokenizer.batch_decode(lowercase__      )

									self.assertListEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									self.assertListEqual(
									    processor.model_input_names		,     feature_extractor.model_input_names		,     msg='`processor` and `feature_extractor` model input names do not match'		,     )


"""simple docstring"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case			       =		logging.getLogger(__name__)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'      )
					parser.add_argument(
					    '--dataset_name',					type=SCREAMING_SNAKE_CASE_,					default='wikitext',					help='Name of the training. Explore datasets at: hf.co/datasets.',					)
					parser.add_argument(
					    '--dataset_config',					type=SCREAMING_SNAKE_CASE_,					default='wikitext-103-raw-v1',					help='Configuration name of the dataset.'      )
					parser.add_argument(
					    '--batch_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_0,					help='Batch size during training.',					)
					parser.add_argument(
					    '--vocab_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_4_8,					help='Size of the desired vocabulary.',					)
					parser.add_argument(
					    '--limit',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Limit the number of shards (used for debugging).',					)
					parser.add_argument(
					    '--export_to_hub',					action='store_true',					)

					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'      )

					if args.limit is not None:
										SCREAMING_SNAKE_CASE           =     min(len(SCREAMING_SNAKE_CASE_      ),					args.limit      )
										SCREAMING_SNAKE_CASE           =     dataset.select(range(SCREAMING_SNAKE_CASE_      )      )
										logger.info(f'''Limiting the dataset to {args.limit} entries.'''      )

					def batch_iterator():
										for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					args.batch_size      ):
															yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
					SCREAMING_SNAKE_CASE           =     Tokenizer(Unigram()      )
					SCREAMING_SNAKE_CASE           =     normalizers.Sequence([normalizers.Replace('``',					'"'      ), normalizers.Replace('\'\'',					'"'      )]      )
					SCREAMING_SNAKE_CASE           =     pre_tokenizers.Metaspace()

					# Prepare the trainer.
					SCREAMING_SNAKE_CASE           =     UnigramTrainer(
					    unk_token='<unk>',					special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]'],					vocab_size=args.vocab_size,					)

					logger.info('Training the tokenizer.'      )
					tokenizer.train_from_iterator(batch_iterator(),					trainer=SCREAMING_SNAKE_CASE_      )
					logger.info('Tokenizer training complete!'      )

					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[CLS]'      )
					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[SEP]'      )
					SCREAMING_SNAKE_CASE           =     processors.TemplateProcessing(
					    single='[CLS]:0 $A:0 [SEP]:0',					pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1',					special_tokens=[
					        ('[CLS]', cls_token_id),
					        ('[SEP]', sep_token_id),
					    ],					)
					SCREAMING_SNAKE_CASE           =     decoders.Metaspace()

					if args.export_to_hub:
										logger.info('Exporting the trained tokenzier to Hub.'      )
										SCREAMING_SNAKE_CASE           =     AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_      )
										new_tokenizer.push_to_hub('unigram-tokenizer-dataset'      )


if __name__ == "__main__":
	snake_case			       =		parse_args()
	main(args)
