
import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							HerbertTokenizer
       UpperCamelCase_				 =							HerbertTokenizerFast
       UpperCamelCase_				 =							True



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   Any:
         super().setUp()

         # Use a simpler test file without japanese/chinese characters
         with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''  ,      encoding='utf-8'       ) as f_data:
           _lowerCAmelCase						=		f_data.read().replace('\n\n'  ,      '\n'       ).strip()

         _lowerCAmelCase						=		[
             '<s>',
             '</s>',
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             ',</w>',
             '<unk>',
         ]
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

         _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']       )
         _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']       )
         with open(self.vocab_file  ,      'w'       ) as fp:
           fp.write(json.dumps(_lowerCAmelCase       )       )
         with open(self.merges_file  ,      'w'       ) as fp:
           fp.write('\n'.join(_lowerCAmelCase       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			Any       )					->   Dict:
         _lowerCAmelCase						=		'lower newer'
         _lowerCAmelCase						=		'lower newer'
         return input_text, output_text



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Optional[int]:
         _lowerCAmelCase						=		self.tokenizer_class(vocab_file=self.vocab_file  ,      merges_file=self.merges_file       )

         _lowerCAmelCase						=		'lower'
         _lowerCAmelCase						=		['low', 'er</w>']
         _lowerCAmelCase						=		tokenizer.tokenize(_lowerCAmelCase       )
         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		tokens + ['<unk>']
         _lowerCAmelCase						=		[16, 17, 23]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         if not self.test_rust_tokenizer:
           return

         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_rust_tokenizer()

         _lowerCAmelCase						=		'lower,newer'

         _lowerCAmelCase						=		tokenizer.tokenize(_lowerCAmelCase       )
         _lowerCAmelCase						=		rust_tokenizer.tokenize(_lowerCAmelCase       )
         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer.encode(_lowerCAmelCase  ,      add_special_tokens=_lowerCAmelCase       )
         _lowerCAmelCase						=		rust_tokenizer.encode(_lowerCAmelCase  ,      add_special_tokens=_lowerCAmelCase       )
         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		self.get_rust_tokenizer()
         _lowerCAmelCase						=		tokenizer.encode(_lowerCAmelCase       )
         _lowerCAmelCase						=		rust_tokenizer.encode(_lowerCAmelCase       )
         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Tuple:
         _lowerCAmelCase						=		self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'       )

         _lowerCAmelCase						=		tokenizer.encode('konstruowanie sekwencji'  ,      add_special_tokens=_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.encode('konstruowanie wielu sekwencji'  ,      add_special_tokens=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase  ,      _lowerCAmelCase       )

         assert encoded_sentence == [0] + text + [2]
         assert encoded_pair == [0] + text + [2] + text_a + [2]


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


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





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


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

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

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

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

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

         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(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'  ,      )
