

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


A_						:     Any			  =				get_tests_dir('fixtures/test_sentencepiece.model')
@require_torch
@require_torchaudio
@require_sentencepiece
class    A_				( unittest.TestCase  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> List[Any]:
        __UpperCAmelCase			   =     tempfile.mkdtemp()

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

        __UpperCAmelCase			   =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
        tokenizer.save_pretrained(self.tmpdirname					)

        __UpperCAmelCase			   =     {
            '''feature_size''': 24,
            '''num_mel_bins''': 24,
            '''padding_value''': 0.0,
            '''sampling_rate''': 16_000,
            '''return_attention_mask''': False,
            '''do_normalize''': True,
        }
        save_json(lowercase__       , save_dir / FEATURE_EXTRACTOR_NAME					)


    def        lowerCAmelCase_  (self       , **lowercase__					)       -> Union[str, Any]:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       , **lowercase__					)


    def        lowerCAmelCase_  (self       , **lowercase__					)       -> Tuple:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       , **lowercase__					)


    def        lowerCAmelCase_  (self					)       -> List[str]:
        shutil.rmtree(self.tmpdirname					)


    def        lowerCAmelCase_  (self					)       -> Dict:
        __UpperCAmelCase			   =     self.get_tokenizer()
        __UpperCAmelCase			   =     self.get_feature_extractor()

        __UpperCAmelCase			   =     SpeechaTextProcessor(tokenizer=lowercase__       , feature_extractor=lowercase__					)

        processor.save_pretrained(self.tmpdirname					)
        __UpperCAmelCase			   =     SpeechaTextProcessor.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        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()       , feature_extractor=self.get_feature_extractor()					)
        processor.save_pretrained(self.tmpdirname					)

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

        __UpperCAmelCase			   =     SpeechaTextProcessor.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        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     self.get_feature_extractor()
        __UpperCAmelCase			   =     self.get_tokenizer()

        __UpperCAmelCase			   =     SpeechaTextProcessor(tokenizer=lowercase__       , feature_extractor=lowercase__					)

        __UpperCAmelCase			   =     floats_list((3, 1_000)					)

        __UpperCAmelCase			   =     feature_extractor(lowercase__       , return_tensors='''np'''					)
        __UpperCAmelCase			   =     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        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     self.get_feature_extractor()
        __UpperCAmelCase			   =     self.get_tokenizer()

        __UpperCAmelCase			   =     SpeechaTextProcessor(tokenizer=lowercase__       , feature_extractor=lowercase__					)

        __UpperCAmelCase			   =     '''This is a test string'''

        __UpperCAmelCase			   =     processor(text=lowercase__					)

        __UpperCAmelCase			   =     tokenizer(lowercase__					)

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


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     self.get_feature_extractor()
        __UpperCAmelCase			   =     self.get_tokenizer()

        __UpperCAmelCase			   =     SpeechaTextProcessor(tokenizer=lowercase__       , feature_extractor=lowercase__					)

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

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

        self.assertListEqual(lowercase__       , lowercase__					)






    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     self.get_feature_extractor()
        __UpperCAmelCase			   =     self.get_tokenizer()

        __UpperCAmelCase			   =     SpeechaTextProcessor(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'''       , )







# 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/




