

'''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


lowerCamelCase			:				int 			=					get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class 							A__   (	A__					,       unittest.TestCase  ):
  A__					=    ReformerTokenizer
  A__					=    ReformerTokenizerFast
  A__					=    True
  A__					=    False
  A__					=    True





  def 						A  (		self				:     Any			)     ->   Any:





       '''simple docstring'''






       super().setUp()

       _SCREAMING_SNAKE_CASE							   =ReformerTokenizer(_snake_case							, keep_accents=_snake_case			)
       tokenizer.save_pretrained(self.tmpdirname			)





  def 						A  (		self				:     Tuple			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='<s>'
       _SCREAMING_SNAKE_CASE							   =1

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





  def 						A  (		self				:     Tuple			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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(_snake_case			)							, 1000			)





  def 						A  (		self				:     Optional[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       self.assertEqual(self.get_tokenizer().vocab_size							, 1000			)





  def 						A  (		self				:     str			)     ->   List[str]:





       '''simple docstring'''






       if not self.test_rust_tokenizer:
            return

       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer()
       _SCREAMING_SNAKE_CASE							   =self.get_rust_tokenizer()

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

       _SCREAMING_SNAKE_CASE							   =tokenizer.tokenize(_snake_case			)
       _SCREAMING_SNAKE_CASE							   =rust_tokenizer.tokenize(_snake_case			)
       self.assertListEqual(_snake_case							, _snake_case			)

       _SCREAMING_SNAKE_CASE							   =tokenizer.encode(_snake_case							, add_special_tokens=_snake_case			)
       _SCREAMING_SNAKE_CASE							   =rust_tokenizer.encode(_snake_case							, add_special_tokens=_snake_case			)
       self.assertListEqual(_snake_case							, _snake_case			)

       _SCREAMING_SNAKE_CASE							   =self.get_rust_tokenizer()
       _SCREAMING_SNAKE_CASE							   =tokenizer.encode(_snake_case			)
       _SCREAMING_SNAKE_CASE							   =rust_tokenizer.encode(_snake_case			)
       self.assertListEqual(_snake_case							, _snake_case			)





  def 						A  (		self				:     Tuple							, _a				:     Any=15			)     ->   Dict:





       '''simple docstring'''






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                 _SCREAMING_SNAKE_CASE							   =self.rust_tokenizer_class.from_pretrained(_snake_case							, **_snake_case			)

                 # Simple input
                 _SCREAMING_SNAKE_CASE							   ='This is a simple input'
                 _SCREAMING_SNAKE_CASE							   =['This is a simple input 1', 'This is a simple input 2']
                 _SCREAMING_SNAKE_CASE							   =('This is a simple input', 'This is a pair')
                 _SCREAMING_SNAKE_CASE							   =[
                     ('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(_snake_case							, tokenizer_r.encode							, _snake_case							, max_length=_snake_case							, padding='max_length'			)

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

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

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

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

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





  def 						A  (		self				:     Optional[Any]			)     ->   Any:





       '''simple docstring'''






       pass





  def 						A  (		self				:     List[str]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ReformerTokenizer(_snake_case							, keep_accents=_snake_case			)

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

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

       _SCREAMING_SNAKE_CASE							   =tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
       self.assertListEqual(
           _snake_case							, [
               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',
               '.',
           ]							, )
       _SCREAMING_SNAKE_CASE							   =tokenizer.convert_tokens_to_ids(_snake_case			)
       self.assertListEqual(
           _snake_case							, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							, )

       _SCREAMING_SNAKE_CASE							   =tokenizer.convert_ids_to_tokens(_snake_case			)
       self.assertListEqual(
           _snake_case							, [
               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 						A  (		self				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'			)





  @slow
  def 						A  (		self				:     List[Any]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='Hello World!'
       _SCREAMING_SNAKE_CASE							   =[126, 32, 262, 152, 38, 72, 287]

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





  @slow
  def 						A  (		self				:     Any			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =(
           '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'
       )
       _SCREAMING_SNAKE_CASE							   =[
           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(_snake_case							, self.big_tokenizer.encode(_snake_case			)			)





  @require_torch
  @slow
  def 						A  (		self				:     List[str]			)     ->   Union[str, Any]:





       '''simple docstring'''






       import torch

       from transformers import ReformerConfig, ReformerModel

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

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

       # 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(**_snake_case			)
            model(**_snake_case			)





  @slow
  def 						A  (		self				:     int			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ={'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
       _SCREAMING_SNAKE_CASE							   =[
           'This is a very simple sentence.',
           'The quick brown fox jumps over the lazy dog.',
       ]

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



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
