






"""simple docstring"""





import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE					:				Dict					    =						get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class      a						(					_UpperCAmelCase  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Any						= ReformerTokenizer
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ReformerTokenizerFast
    SCREAMING_SNAKE_CASE						:     Optional[int]						= True
    SCREAMING_SNAKE_CASE						:     str						= False
    SCREAMING_SNAKE_CASE						:     List[Any]						= True






    def    UpperCamelCase       (       self     :							List[str]					)     ->		str:
      super().setUp()

      lowerCamelCase_         =						ReformerTokenizer(_UpperCAmelCase       ,						keep_accents=_UpperCAmelCase					)
      tokenizer.save_pretrained(self.tmpdirname					)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		str:
      lowerCamelCase_         =						'''<s>'''
      lowerCamelCase_         =						1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase					)       ,						_UpperCAmelCase					)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase					)       ,						_UpperCAmelCase					)






    def    UpperCamelCase       (       self     :							Any					)     ->		List[str]:
      lowerCamelCase_         =						list(self.get_tokenizer().get_vocab().keys()					)

      self.assertEqual(vocab_keys[0]       ,						'<unk>'					)
      self.assertEqual(vocab_keys[1]       ,						'<s>'					)
      self.assertEqual(vocab_keys[-1]       ,						'j'					)
      self.assertEqual(len(_UpperCAmelCase					)       ,						1000					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Dict:
      self.assertEqual(self.get_tokenizer().vocab_size       ,						1000					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Union[str, Any]:
      if not self.test_rust_tokenizer:
        return

      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      lowerCamelCase_         =						'''I was born in 92000, and this is falsé.'''

      lowerCamelCase_         =						tokenizer.tokenize(_UpperCAmelCase					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(_UpperCAmelCase					)
      self.assertListEqual(_UpperCAmelCase       ,						_UpperCAmelCase					)

      lowerCamelCase_         =						tokenizer.encode(_UpperCAmelCase       ,						add_special_tokens=_UpperCAmelCase					)
      lowerCamelCase_         =						rust_tokenizer.encode(_UpperCAmelCase       ,						add_special_tokens=_UpperCAmelCase					)
      self.assertListEqual(_UpperCAmelCase       ,						_UpperCAmelCase					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(_UpperCAmelCase					)
      lowerCamelCase_         =						rust_tokenizer.encode(_UpperCAmelCase					)
      self.assertListEqual(_UpperCAmelCase       ,						_UpperCAmelCase					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Any=15					)     ->		List[str]:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase       ,						**_UpperCAmelCase					)

          # Simple input
          lowerCamelCase_         =						'''This is a simple input'''
          lowerCamelCase_         =						['''This is a simple input 1''', '''This is a simple input 2''']
          lowerCamelCase_         =						('''This is a simple input''', '''This is a pair''')
          lowerCamelCase_         =						[
              ('''This is a simple input 1''', '''This is a simple input 2'''),
              ('''This is a simple pair 1''', '''This is a simple pair 2'''),
          ]

          # Simple input tests
          self.assertRaises(_UpperCAmelCase       ,						tokenizer_r.encode       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'					)

          # Simple input
          self.assertRaises(_UpperCAmelCase       ,						tokenizer_r.encode_plus       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'					)

          # Simple input
          self.assertRaises(
              _UpperCAmelCase       ,						tokenizer_r.batch_encode_plus       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'       ,						)

          # Pair input
          self.assertRaises(_UpperCAmelCase       ,						tokenizer_r.encode       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'					)

          # Pair input
          self.assertRaises(_UpperCAmelCase       ,						tokenizer_r.encode_plus       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'					)

          # Pair input
          self.assertRaises(
              _UpperCAmelCase       ,						tokenizer_r.batch_encode_plus       ,						_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						padding='max_length'       ,						)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Dict:
      pass






    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      lowerCamelCase_         =						ReformerTokenizer(_UpperCAmelCase       ,						keep_accents=_UpperCAmelCase					)

      lowerCamelCase_         =						tokenizer.tokenize('This is a test'					)
      self.assertListEqual(_UpperCAmelCase       ,						['▁This', '▁is', '▁a', '▁t', 'est']					)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)       ,						[285, 46, 10, 170, 382]       ,						)

      lowerCamelCase_         =						tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
      self.assertListEqual(
          _UpperCAmelCase       ,						[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]       ,						)
      lowerCamelCase_         =						tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)
      self.assertListEqual(
          _UpperCAmelCase       ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,						)

      lowerCamelCase_         =						tokenizer.convert_ids_to_tokens(_UpperCAmelCase					)
      self.assertListEqual(
          _UpperCAmelCase       ,						[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]       ,						)






    @cached_property
    def    UpperCamelCase       (       self     :							Dict					)     ->		str:
      return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'					)






    @slow
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[str]:
      lowerCamelCase_         =						'''Hello World!'''
      lowerCamelCase_         =						[126, 32, 262, 152, 38, 72, 287]

      self.assertListEqual(_UpperCAmelCase       ,						self.big_tokenizer.encode(_UpperCAmelCase					)					)






    @slow
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Tuple:
      lowerCamelCase_         =						(
          '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
          ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
      )
      lowerCamelCase_         =						[
          108,
          265,
          24,
          111,
          4,
          258,
          156,
          35,
          28,
          275,
          3,
          259,
          297,
          260,
          84,
          4,
          35,
          110,
          44,
          8,
          259,
          91,
          268,
          21,
          11,
          209,
          274,
          109,
          266,
          277,
          117,
          86,
          93,
          315,
          258,
          278,
          258,
          277,
          258,
          0,
          258,
          288,
          258,
          319,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          287,
          258,
          315,
          258,
          289,
          258,
          278,
          99,
          269,
          266,
          262,
          8,
          259,
          241,
          4,
          217,
          230,
          268,
          266,
          55,
          168,
          106,
          75,
          193,
          266,
          223,
          27,
          49,
          26,
          282,
          25,
          264,
          299,
          19,
          26,
          0,
          258,
          277,
          117,
          86,
          93,
          176,
          183,
          270,
          11,
          262,
          42,
          61,
          265,
      ]

      self.assertListEqual(_UpperCAmelCase       ,						self.big_tokenizer.encode(_UpperCAmelCase					)					)






    @require_torch
    @slow
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[str]:
      import torch

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      lowerCamelCase_         =						list(self.big_tokenizer.get_vocab().keys()					)[:10]
      lowerCamelCase_         =						''' '''.join(_UpperCAmelCase					)
      lowerCamelCase_         =						self.big_tokenizer.encode_plus(_UpperCAmelCase       ,						return_tensors='pt'					)
      lowerCamelCase_         =						self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,						return_tensors='pt'					)

      lowerCamelCase_         =						ReformerConfig()
      # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
      lowerCamelCase_         =						encoded_sequence['''input_ids'''].shape
      lowerCamelCase_         =						ReformerModel(_UpperCAmelCase					)

      # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
        model(**_UpperCAmelCase					)
        model(**_UpperCAmelCase					)






    @slow
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		str:
      # fmt: off
      lowerCamelCase_         =						{'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # This tokenizer does not know some characters like ")".
      # That is the reason why we use very simple texts here.
      # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
      lowerCamelCase_         =						[
          '''This is a very simple sentence.''',
          '''The quick brown fox jumps over the lazy dog.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=_UpperCAmelCase       ,						model_name='google/reformer-crime-and-punishment'       ,						revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'       ,						padding=_UpperCAmelCase       ,						sequences=_UpperCAmelCase       ,						)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A			   = {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SpeechT5ForSpeechToText",
           "SpeechT5ForSpeechToSpeech",
           "SpeechT5ForTextToSpeech",
           "SpeechT5Model",
           "SpeechT5PreTrainedModel",
           "SpeechT5HifiGan",
       ]

if TYPE_CHECKING:
       from .configuration_speechta import (
           SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
           SpeechTaConfig,
           SpeechTaHifiGanConfig,
       )
       from .feature_extraction_speechta import SpeechTaFeatureExtractor
       from .processing_speechta import SpeechTaProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speechta import SpeechTaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speechta import (
                  SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechTaForSpeechToSpeech,
                  SpeechTaForSpeechToText,
                  SpeechTaForTextToSpeech,
                  SpeechTaHifiGan,
                  SpeechTaModel,
                  SpeechTaPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
