

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import WhisperFeatureExtractor

if is_torch_available():
    import torch

lowerCAmelCase     :   Optional[int]						     =  random.Random()

def    A_  (							a					,   a=1.0					,   a=None					,   a=None  ):

      """simple docstring"""



      if rng is None:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     global_rng

      SCREAMING_SNAKE_CASE_				:  List[str]           =     []
      for batch_idx in range(shape[0]  ):
            values.append([]  )
            for _ in range(shape[1]  ):
                  values[-1].append(rng.random() * scale  )

      return values
@require_torch
@require_torchaudio
class  _A		(  unittest.TestCase):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=7 ,					_SCREAMING_SNAKE_CASE=400 ,					_SCREAMING_SNAKE_CASE=2000 ,					_SCREAMING_SNAKE_CASE=10 ,					_SCREAMING_SNAKE_CASE=160 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=4000 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     parent
        SCREAMING_SNAKE_CASE_				:  int           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     min_seq_length
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     max_seq_length
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     padding_value
        SCREAMING_SNAKE_CASE_				:  str           =     sampling_rate
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     return_attention_mask
        SCREAMING_SNAKE_CASE_				:  Dict           =     do_normalize
        SCREAMING_SNAKE_CASE_				:  Any           =     feature_size
        SCREAMING_SNAKE_CASE_				:  int           =     chunk_length
        SCREAMING_SNAKE_CASE_				:  str           =     hop_length

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        def _flatten(_SCREAMING_SNAKE_CASE					):
              return list(itertools.chain(*_SCREAMING_SNAKE_CASE					)					)

        if equal_length:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
        else:
              # make sure that inputs increase in size
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  floats_list((x, self.feature_size)					)
                  for x in range(self.min_seq_length ,					self.max_seq_length ,					self.seq_length_diff					)
              ]
        if numpify:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [np.asarray(_SCREAMING_SNAKE_CASE					) for x in speech_inputs]
        return speech_inputs

@require_torch
@require_torchaudio
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  WhisperFeatureExtractor if is_speech_available() else None

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     WhisperFeatureExtractionTester(self					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.feature_extraction_class(**self.feat_extract_dict					)

        with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE					)[0]
              check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     feat_extract_first.to_dict()
        SCREAMING_SNAKE_CASE_				:  Dict           =     feat_extract_second.to_dict()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     feat_extract_first.mel_filters
        SCREAMING_SNAKE_CASE_				:  str           =     feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.feature_extraction_class(**self.feat_extract_dict					)

        with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_				:  Dict           =     os.path.join(_SCREAMING_SNAKE_CASE ,					'feat_extract.json'					)
              feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     feat_extract_first.to_dict()
        SCREAMING_SNAKE_CASE_				:  int           =     feat_extract_second.to_dict()
        SCREAMING_SNAKE_CASE_				:  Any           =     feat_extract_first.mel_filters
        SCREAMING_SNAKE_CASE_				:  Dict           =     feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        # create three inputs of length 800, 1000, and 1200
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [floats_list((1, x)					)[0] for x in range(800 ,					1400 ,					200					)]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [np.asarray(_SCREAMING_SNAKE_CASE					) for speech_input in speech_inputs]

        # Test feature size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					padding='max_length' ,					return_tensors='np'					).input_features
        self.assertTrue(input_features.ndim == 3					)
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames					)
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size					)

        # Test not batched input
        SCREAMING_SNAKE_CASE_				:  Dict           =     feature_extractor(speech_inputs[0] ,					return_tensors='np'					).input_features
        SCREAMING_SNAKE_CASE_				:  int           =     feature_extractor(np_speech_inputs[0] ,					return_tensors='np'					).input_features
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)

        # Test batched
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)

        # Test 2-D numpy arrays are batched.
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [floats_list((1, x)					)[0] for x in (800, 800, 800)]
        SCREAMING_SNAKE_CASE_				:  str           =     np.asarray(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)

        # Test truncation required
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [floats_list((1, x)					)[0] for x in range(200 ,					(feature_extractor.n_samples + 500) ,					200					)]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [np.asarray(_SCREAMING_SNAKE_CASE					) for speech_input in speech_inputs]

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [x[: feature_extractor.n_samples] for x in speech_inputs]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [np.asarray(_SCREAMING_SNAKE_CASE					) for speech_input in speech_inputs_truncated]

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='np'					).input_features
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        import torch

        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     np.random.rand(100 ,					32					).astype(np.floataa					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              SCREAMING_SNAKE_CASE_				:  int           =     feature_extractor.pad([{'input_features': inputs}] ,					return_tensors='np'					)
              self.assertTrue(np_processed.input_features.dtype == np.floataa					)
              SCREAMING_SNAKE_CASE_				:  int           =     feature_extractor.pad([{'input_features': inputs}] ,					return_tensors='pt'					)
              self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     load_dataset('hf-internal-testing/librispeech_asr_dummy' ,					'clean' ,					split='validation'					)
        # automatic decoding with librispeech
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ds.sort('id'					).select(range(_SCREAMING_SNAKE_CASE					)					)[:num_samples]['audio']

        return [x["array"] for x in speech_samples]

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     torch.tensor(
            [
                0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
            ]					)
        # fmt: on

        SCREAMING_SNAKE_CASE_				:  Tuple           =     self._load_datasamples(1					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     WhisperFeatureExtractor()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     feature_extractor(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).input_features
        self.assertEqual(input_features.shape ,					(1, 80, 3000)					)
        self.assertTrue(torch.allclose(input_features[0, 0, :30] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self._load_datasamples(1					)[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
        SCREAMING_SNAKE_CASE_				:  List[str]           =     feat_extract.zero_mean_unit_var_norm([audio] ,					attention_mask=_SCREAMING_SNAKE_CASE					)[0]

        self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE					) < 1e-3					)					)
        self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE					) - 1					) < 1e-3					)					)









import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Dict       =  GPTSanJapaneseTokenizer
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  {'''do_clean_text''': False, '''add_prefix_space''': False}

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().setUp()

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
        # fmt: on
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
        SCREAMING_SNAKE_CASE_				:  int           =     {'unk_token': '<unk>'}

        SCREAMING_SNAKE_CASE_				:  Any           =     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['vocab_file']					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['emoji_file']					)
        with open(self.vocab_file ,					'w' ,					encoding='utf-8'					) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)
        with open(self.emoji_file ,					'w'					) as emoji_writer:
              emoji_writer.write(json.dumps(_SCREAMING_SNAKE_CASE					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        kwargs.update(self.special_tokens_map					)
        return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'こんにちは、世界。 \nこんばんは、世界。😀'
        return input_text, output_text

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_input_output_texts(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer.decode(_SCREAMING_SNAKE_CASE ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE					)
        return text, ids

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass  # TODO add if relevant

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_tokenizer()

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'こんにちは、世界。　こんばんは、㔺界。'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        # Testing conversion to ids without special tokens
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        # Testing conversion to ids with special tokens
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_				:  str           =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_tokenizer()

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
        SCREAMING_SNAKE_CASE_				:  str           =     'こんにちは、、、、世界。こんばんは、、、、世界。'
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  int           =     'こんにちは、世界。'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     'こんばんは、㔺界。😀'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     'こんにちは、世界。こんばんは、世界。😀'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode(prefix_text + input_text					)
        SCREAMING_SNAKE_CASE_				:  int           =     tokenizer.encode('' ,					prefix_text=prefix_text + input_text					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.encode(_SCREAMING_SNAKE_CASE ,					prefix_text=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.decode(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        # Testing tokenization
        SCREAMING_SNAKE_CASE_				:  Any           =     'こんにちは、世界。'
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'こんばんは、㔺界。😀'

        SCREAMING_SNAKE_CASE_				:  int           =     len(tokenizer.encode(_SCREAMING_SNAKE_CASE					)					) - 2
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(tokenizer.encode(_SCREAMING_SNAKE_CASE					)					) - 2

        SCREAMING_SNAKE_CASE_				:  str           =     [1] + [0] * (len_prefix + len_text + 1)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [1] * (len_prefix + len_text + 1) + [0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [1] + [1] * (len_prefix) + [0] * (len_text + 1)

        SCREAMING_SNAKE_CASE_				:  Any           =     tokenizer(prefix_text + input_text					).token_type_ids
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer('' ,					prefix_text=prefix_text + input_text					).token_type_ids
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer(_SCREAMING_SNAKE_CASE ,					prefix_text=_SCREAMING_SNAKE_CASE					).token_type_ids
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer.encode('あンいワ'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     tokenizer.encode('' ,					prefix_text='あンいワ'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer.encode('いワ' ,					prefix_text='あン'					)

        self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE					) ,					tokenizer.decode(_SCREAMING_SNAKE_CASE					)					)
        self.assertEqual(tokenizer.decode(_SCREAMING_SNAKE_CASE					) ,					tokenizer.decode(_SCREAMING_SNAKE_CASE					)					)
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertNotEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.assertEqual(x_token_a[1] ,					x_token_a[-1]					)  # SEG token
        self.assertEqual(x_token_a[1] ,					x_token_a[3]					)  # SEG token

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer(_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE					)

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
        SCREAMING_SNAKE_CASE_				:  int           =     [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
        SCREAMING_SNAKE_CASE_				:  Any           =     [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
        # fmt: on
        self.assertListEqual(x_token.input_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token.token_type_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token.attention_mask ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.input_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.token_type_ids ,					_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(x_token_a.attention_mask ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass






