




"""simple docstring"""





import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_lowercase							=			get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_torch
@require_torchaudio
@require_sentencepiece
class 					__lowercase				(	unittest.TestCase		):


				'''simple docstring'''


				def 					_lowerCamelCase    (      self      :   Any					):
										snake_case__				:    Optional[Any]								=    tempfile.mkdtemp()

										snake_case__				:    List[str]								=    ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
										snake_case__				:    Optional[Any]								=    dict(zip(SCREAMING_SNAKE_CASE_					,range(len(SCREAMING_SNAKE_CASE_					)					)					)					)
										snake_case__				:    int								=    Path(self.tmpdirname					)
										save_json(SCREAMING_SNAKE_CASE_					,save_dir / VOCAB_FILES_NAMES['''vocab_file''']					)
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(SCREAMING_SNAKE_CASE_					,save_dir / VOCAB_FILES_NAMES['''spm_file''']					)

										snake_case__				:    Optional[int]								=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
										tokenizer.save_pretrained(self.tmpdirname					)

										snake_case__				:    Optional[int]								=    {
										    '''feature_size''': 2_4,
										    '''num_mel_bins''': 2_4,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}
										save_json(SCREAMING_SNAKE_CASE_					,save_dir / FEATURE_EXTRACTOR_NAME					)

				def 					_lowerCamelCase    (      self      :   int					,**SCREAMING_SNAKE_CASE_      :   int					):
										return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,**SCREAMING_SNAKE_CASE_					)

				def 					_lowerCamelCase    (      self      :   Union[str, Any]					,**SCREAMING_SNAKE_CASE_      :   str					):
										return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,**SCREAMING_SNAKE_CASE_					)

				def 					_lowerCamelCase    (      self      :   Optional[int]					):
										shutil.rmtree(self.tmpdirname					)

				def 					_lowerCamelCase    (      self      :   Union[str, Any]					):
										snake_case__				:    Any								=    self.get_tokenizer()
										snake_case__				:    str								=    self.get_feature_extractor()

										snake_case__				:    List[str]								=    SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,feature_extractor=SCREAMING_SNAKE_CASE_					)

										processor.save_pretrained(self.tmpdirname					)
										snake_case__				:    Optional[int]								=    SpeechaTextProcessor.from_pretrained(self.tmpdirname					)

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

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

				def 					_lowerCamelCase    (      self      :   Optional[int]					):
										snake_case__				:    Optional[int]								=    SpeechaTextProcessor(
										    tokenizer=self.get_tokenizer()					,feature_extractor=self.get_feature_extractor()					)
										processor.save_pretrained(self.tmpdirname					)

										snake_case__				:    Optional[int]								=    self.get_tokenizer(bos_token='''(BOS)'''					,eos_token='''(EOS)'''					)
										snake_case__				:    int								=    self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_					,padding_value=1.0					)

										snake_case__				:    Optional[Any]								=    SpeechaTextProcessor.from_pretrained(
										    self.tmpdirname					,bos_token='''(BOS)'''					,eos_token='''(EOS)'''					,do_normalize=SCREAMING_SNAKE_CASE_					,padding_value=1.0					)

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

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

				def 					_lowerCamelCase    (      self      :   Optional[int]					):
										snake_case__				:    Dict								=    self.get_feature_extractor()
										snake_case__				:    Any								=    self.get_tokenizer()

										snake_case__				:    str								=    SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,feature_extractor=SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Optional[int]								=    floats_list((3, 1_0_0_0)					)

										snake_case__				:    Optional[int]								=    feature_extractor(SCREAMING_SNAKE_CASE_					,return_tensors='''np'''					)
										snake_case__				:    List[str]								=    processor(SCREAMING_SNAKE_CASE_					,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 					_lowerCamelCase    (      self      :   Dict					):
										snake_case__				:    int								=    self.get_feature_extractor()
										snake_case__				:    Optional[Any]								=    self.get_tokenizer()

										snake_case__				:    List[Any]								=    SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,feature_extractor=SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Any								=    '''This is a test string'''

										snake_case__				:    List[str]								=    processor(text=SCREAMING_SNAKE_CASE_					)

										snake_case__				:    List[str]								=    tokenizer(SCREAMING_SNAKE_CASE_					)

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

				def 					_lowerCamelCase    (      self      :   Tuple					):
										snake_case__				:    List[str]								=    self.get_feature_extractor()
										snake_case__				:    Dict								=    self.get_tokenizer()

										snake_case__				:    List[Any]								=    SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,feature_extractor=SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Any								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										snake_case__				:    Union[str, Any]								=    processor.batch_decode(SCREAMING_SNAKE_CASE_					)
										snake_case__				:    List[str]								=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					)

										self.assertListEqual(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)





				def 					_lowerCamelCase    (      self      :   List[Any]					):
										snake_case__				:    Tuple								=    self.get_feature_extractor()
										snake_case__				:    List[str]								=    self.get_tokenizer()

										snake_case__				:    int								=    SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,feature_extractor=SCREAMING_SNAKE_CASE_					)

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



from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




