





"""simple docstring"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/










"""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 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<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(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

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






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

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

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






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

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

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






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					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						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

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






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

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



