'''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


_UpperCAmelCase    :     List[str]         =       get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   ReformerTokenizer
   __lowerCAmelCase         =   ReformerTokenizerFast
   __lowerCAmelCase         =   True
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   True
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        super().setUp()

        _A    		=  ReformerTokenizer(UpperCamelCase__, keep_accents=UpperCamelCase__     )
        tokenizer.save_pretrained(self.tmpdirname     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  '<s>'
        _A    		=  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      :			List[Any]     )  ->			str:
        _A    		=  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__     ), 10_00     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        self.assertEqual(self.get_tokenizer().vocab_size, 10_00     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			int:
        if not self.test_rust_tokenizer:
             return

        _A    		=  self.get_tokenizer()
        _A    		=  self.get_rust_tokenizer()

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

        _A    		=  tokenizer.tokenize(UpperCamelCase__     )
        _A    		=  rust_tokenizer.tokenize(UpperCamelCase__     )
        self.assertListEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  tokenizer.encode(UpperCamelCase__, add_special_tokens=UpperCamelCase__     )
        _A    		=  rust_tokenizer.encode(UpperCamelCase__, add_special_tokens=UpperCamelCase__     )
        self.assertListEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  self.get_rust_tokenizer()
        _A    		=  tokenizer.encode(UpperCamelCase__     )
        _A    		=  rust_tokenizer.encode(UpperCamelCase__     )
        self.assertListEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			int=15     )  ->			str:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                  _A    		=  self.rust_tokenizer_class.from_pretrained(UpperCamelCase__, **UpperCamelCase__     )

                  # Simple input
                  _A    		=  'This is a simple input'
                  _A    		=  ['This is a simple input 1', 'This is a simple input 2']
                  _A    		=  ('This is a simple input', 'This is a pair')
                  _A    		=  [
                      ('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]     )  ->			List[Any]:
        pass
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  ReformerTokenizer(UpperCamelCase__, keep_accents=UpperCamelCase__     )

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

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(UpperCamelCase__     ), [2_85, 46, 10, 1_70, 3_82], )

        _A    		=  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',
                '.',
            ], )
        _A    		=  tokenizer.convert_tokens_to_ids(UpperCamelCase__     )
        self.assertListEqual(
            UpperCamelCase__, [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4], )

        _A    		=  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      :			Any     )  ->			str:
        return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'     )
   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[Any]:
        _A    		=  'Hello World!'
        _A    		=  [1_26, 32, 2_62, 1_52, 38, 72, 2_87]

        self.assertListEqual(UpperCamelCase__, self.big_tokenizer.encode(UpperCamelCase__     )     )
   @slow
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        _A    		=  (
            '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'
        )
        _A    		=  [
            1_08,
            2_65,
            24,
            1_11,
            4,
            2_58,
            1_56,
            35,
            28,
            2_75,
            3,
            2_59,
            2_97,
            2_60,
            84,
            4,
            35,
            1_10,
            44,
            8,
            2_59,
            91,
            2_68,
            21,
            11,
            2_09,
            2_74,
            1_09,
            2_66,
            2_77,
            1_17,
            86,
            93,
            3_15,
            2_58,
            2_78,
            2_58,
            2_77,
            2_58,
            0,
            2_58,
            2_88,
            2_58,
            3_19,
            2_58,
            0,
            2_58,
            0,
            2_58,
            0,
            2_58,
            0,
            2_58,
            2_87,
            2_58,
            3_15,
            2_58,
            2_89,
            2_58,
            2_78,
            99,
            2_69,
            2_66,
            2_62,
            8,
            2_59,
            2_41,
            4,
            2_17,
            2_30,
            2_68,
            2_66,
            55,
            1_68,
            1_06,
            75,
            1_93,
            2_66,
            2_23,
            27,
            49,
            26,
            2_82,
            25,
            2_64,
            2_99,
            19,
            26,
            0,
            2_58,
            2_77,
            1_17,
            86,
            93,
            1_76,
            1_83,
            2_70,
            11,
            2_62,
            42,
            61,
            2_65,
        ]

        self.assertListEqual(UpperCamelCase__, self.big_tokenizer.encode(UpperCamelCase__     )     )
   @require_torch
   @slow
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        import torch

        from transformers import ReformerConfig, ReformerModel

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

        _A    		=  ReformerConfig()
        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
        _A    		=  encoded_sequence['input_ids'].shape
        _A    		=  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      :			List[str]     )  ->			Any:
        # fmt: off
        _A    		=  {'input_ids': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], '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
        _A    		=  [
            '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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase    :     List[Any]         =       {
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2TokenizerFast''']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       ['''LayoutLMv2FeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2ImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       [
       '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LayoutLMv2ForQuestionAnswering''',
       '''LayoutLMv2ForSequenceClassification''',
       '''LayoutLMv2ForTokenClassification''',
       '''LayoutLMv2Layer''',
       '''LayoutLMv2Model''',
       '''LayoutLMv2PreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   _UpperCAmelCase    :     List[Any]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



