



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	       =	get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class     lowerCAmelCase_		(  snake_case_	,   unittest.TestCase							):
       UpperCAmelCase__    : int       					=  ReformerTokenizer
       UpperCAmelCase__    : Dict       					=  ReformerTokenizerFast
       UpperCAmelCase__    : Dict       					=  True
       UpperCAmelCase__    : Any       					=  False
       UpperCAmelCase__    : str       					=  True
       def  snake_case_	(      self	)					->		List[str]:
          super().setUp()

          UpperCamelCase   :  Dict						     =     ReformerTokenizer(SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_	)
          tokenizer.save_pretrained(self.tmpdirname	)
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Union[str, Any]						     =     '<s>'
          UpperCamelCase   :  Tuple						     =     1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Union[str, Any]						     =     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(SCREAMING_SNAKE_CASE_	),      1000	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          self.assertEqual(self.get_tokenizer().vocab_size,      1000	)
       def  snake_case_	(      self	)					->		List[str]:





          if not self.test_rust_tokenizer:
             return

          UpperCamelCase   :  Any						     =     self.get_tokenizer()
          UpperCamelCase   :  List[str]						     =     self.get_rust_tokenizer()

          UpperCamelCase   :  Union[str, Any]						     =     'I was born in 92000, and this is falsé.'

          UpperCamelCase   :  Dict						     =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     tokenizer.encode(SCREAMING_SNAKE_CASE_,      add_special_tokens=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_,      add_special_tokens=SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     self.get_rust_tokenizer()
          UpperCamelCase   :  Tuple						     =     tokenizer.encode(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_=15	)					->		str:





          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                UpperCamelCase   :  Optional[int]						     =     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

                # Simple input
                UpperCamelCase   :  int						     =     'This is a simple input'
                UpperCamelCase   :  List[Any]						     =     ['This is a simple input 1', 'This is a simple input 2']
                UpperCamelCase   :  List[Any]						     =     ('This is a simple input', 'This is a pair')
                UpperCamelCase   :  Dict						     =     [
                    ('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(SCREAMING_SNAKE_CASE_,      tokenizer_r.encode,      SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_,      padding='max_length'	)

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

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

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

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

                # Pair input
                self.assertRaises(
                    SCREAMING_SNAKE_CASE_,      tokenizer_r.batch_encode_plus,      SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_,      padding='max_length',      )
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Dict						     =     ReformerTokenizer(SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     tokenizer.tokenize('This is a test'	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      ['▁This', '▁is', '▁a', '▁t', 'est']	)

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

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

          UpperCamelCase   :  Any						     =     tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(
              SCREAMING_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  snake_case_	(      self	)					->		Dict:
          return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'	)
       @slow
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  List[Any]						     =     'Hello World!'
          UpperCamelCase   :  Optional[Any]						     =     [126, 32, 262, 152, 38, 72, 287]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,      self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_	)	)
       @slow
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Union[str, Any]						     =     (
              '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'
          )
          UpperCamelCase   :  Union[str, Any]						     =     [
              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(SCREAMING_SNAKE_CASE_,      self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_	)	)
       @require_torch
       @slow
       def  snake_case_	(      self	)					->		Dict:
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          UpperCamelCase   :  Union[str, Any]						     =     list(self.big_tokenizer.get_vocab().keys()	)[:10]
          UpperCamelCase   :  List[str]						     =     ' '.join(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	)
          UpperCamelCase   :  Union[str, Any]						     =     self.big_tokenizer.batch_encode_plus([sequence, sequence],      return_tensors='pt'	)

          UpperCamelCase   :  Optional[Any]						     =     ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          UpperCamelCase   :  List[Any]						     =     encoded_sequence['input_ids'].shape
          UpperCamelCase   :  int						     =     ReformerModel(SCREAMING_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(**SCREAMING_SNAKE_CASE_	)
             model(**SCREAMING_SNAKE_CASE_	)






       @slow
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     {'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
          UpperCamelCase   :  Tuple						     =     [
              'This is a very simple sentence.',
              'The quick brown fox jumps over the lazy dog.',
          ]

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








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase	       =	abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   from transformers.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(snake_case__     )


def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   from transformers.testing_utils import pytest_terminal_summary_main

   UpperCamelCase   :  Optional[int]						     =     terminalreporter.config.getoption('--make-reports'     )
   if make_reports:
      pytest_terminal_summary_main(snake_case__	,					id=snake_case__     )



