


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


SCREAMING_SNAKE_CASE				:						int			   = get_tests_dir("fixtures/test_sentencepiece.model")






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


   '''simple docstring'''







   def 						UpperCamelCase     (   self						):
        lowercase_				:List[str]       			=				tempfile.mkdtemp()

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

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

        lowercase_				:Any       			=				{
            '''feature_size''': 24,
            '''num_mel_bins''': 24,
            '''padding_value''': 0.0,
            '''sampling_rate''': 1_6000,
            '''return_attention_mask''': False,
            '''do_normalize''': True,
        }
        save_json(UpperCamelCase_      ,							save_dir / FEATURE_EXTRACTOR_NAME						)
   def 						UpperCamelCase     (   self      ,							**UpperCamelCase_						):
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							**UpperCamelCase_						):
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self						):
        shutil.rmtree(self.tmpdirname						)
   def 						UpperCamelCase     (   self						):
        lowercase_				:Any       			=				self.get_tokenizer()
        lowercase_				:int       			=				self.get_feature_extractor()

        lowercase_				:Dict       			=				SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,							feature_extractor=UpperCamelCase_						)

        processor.save_pretrained(self.tmpdirname						)
        lowercase_				:List[Any]       			=				SpeechaTextProcessor.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 						UpperCamelCase     (   self						):
        lowercase_				:int       			=				SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()						)
        processor.save_pretrained(self.tmpdirname						)

        lowercase_				:str       			=				self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''						)
        lowercase_				:Optional[Any]       			=				self.get_feature_extractor(do_normalize=UpperCamelCase_      ,							padding_value=1.0						)

        lowercase_				:List[str]       			=				SpeechaTextProcessor.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 						UpperCamelCase     (   self						):
        lowercase_				:Dict       			=				self.get_feature_extractor()
        lowercase_				:Optional[int]       			=				self.get_tokenizer()

        lowercase_				:List[Any]       			=				SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,							feature_extractor=UpperCamelCase_						)

        lowercase_				:List[Any]       			=				floats_list((3, 1000)						)

        lowercase_				:Dict       			=				feature_extractor(UpperCamelCase_      ,							return_tensors='''np'''						)
        lowercase_				:Union[str, Any]       			=				processor(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 						UpperCamelCase     (   self						):
        lowercase_				:Tuple       			=				self.get_feature_extractor()
        lowercase_				:str       			=				self.get_tokenizer()

        lowercase_				:Tuple       			=				SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,							feature_extractor=UpperCamelCase_						)

        lowercase_				:Dict       			=				'''This is a test string'''

        lowercase_				:str       			=				processor(text=UpperCamelCase_						)

        lowercase_				:Optional[Any]       			=				tokenizer(UpperCamelCase_						)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]						)
   def 						UpperCamelCase     (   self						):
        lowercase_				:Optional[int]       			=				self.get_feature_extractor()
        lowercase_				:Tuple       			=				self.get_tokenizer()

        lowercase_				:Tuple       			=				SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,							feature_extractor=UpperCamelCase_						)

        lowercase_				:Tuple       			=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_				:Union[str, Any]       			=				processor.batch_decode(UpperCamelCase_						)
        lowercase_				:int       			=				tokenizer.batch_decode(UpperCamelCase_						)

        self.assertListEqual(UpperCamelCase_      ,							UpperCamelCase_						)




   def 						UpperCamelCase     (   self						):
        lowercase_				:Dict       			=				self.get_feature_extractor()
        lowercase_				:Union[str, Any]       			=				self.get_tokenizer()

        lowercase_				:Optional[Any]       			=				SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,							feature_extractor=UpperCamelCase_						)

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







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		bool:

    """simple docstring"""


    if not isinstance(lowercase_    ,   lowercase_					):
        __UpperCamelCase     						=  F"Input value of [number={number}] must be an integer"
        raise TypeError(lowercase_					)
    if number < 0 or number % 2 == 1:
        return False
    __UpperCamelCase     						=  int(number**0.5					)
    return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






