


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__UpperCamelCase    :						Any     =							logging.getLogger(__name__)







@dataclass
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							str
   a_				:							List[str]
   a_				:							Optional[List[str]]







@dataclass
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							List[int]
   a_				:							List[int]
   a_				:							Optional[List[int]]    = None
   a_				:							Optional[List[int]]    = None







class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[Any]    = "train"
   a_				:							Optional[int]    = "dev"
   a_				:							List[str]    = "test"







class 							_UpperCamelCase :

   '''simple docstring'''







   @staticmethod
   def   _snake_case    (  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Union[Split, str]							):






         '''simple docstring'''



         raise NotImplementedError

   @staticmethod
   def   _snake_case    (  _lowerCamelCase  : str							):






         '''simple docstring'''



         raise NotImplementedError


   @staticmethod
   def   _snake_case    (  _lowerCamelCase  : List[InputExample]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : PreTrainedTokenizer       ,  _lowerCamelCase  : List[str]=False       ,  _lowerCamelCase  : Any="[CLS]"       ,  _lowerCamelCase  : Tuple=1       ,  _lowerCamelCase  : Any="[SEP]"       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : str=0       ,  _lowerCamelCase  : List[Any]=0       ,  _lowerCamelCase  : int=-1_0_0       ,  _lowerCamelCase  : Any=0       ,  _lowerCamelCase  : Optional[Any]=True       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						{label: i for i, label in enumerate(_lowerCamelCase							)}

         __lowerCamelCase     :      Dict       						=						[]
         for ex_index, example in enumerate(_lowerCamelCase							):
               if ex_index % 1_0_0_0_0 == 0:
                     logger.info("""Writing example %d of %d"""       ,  _lowerCamelCase       ,  len(_lowerCamelCase							)							)

               __lowerCamelCase     :      List[Any]       						=						[]
               __lowerCamelCase     :      Dict       						=						[]
               for word, label in zip(example.words       ,  example.labels							):
                     __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize(_lowerCamelCase							)

                     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                     if len(_lowerCamelCase							) > 0:
                           tokens.extend(_lowerCamelCase							)
                           # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                           label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowerCamelCase							) - 1)							)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
               __lowerCamelCase     :      str       						=						tokenizer.num_special_tokens_to_add()
               if len(_lowerCamelCase							) > max_seq_length - special_tokens_count:
                     __lowerCamelCase     :      Optional[Any]       						=						tokens[: (max_seq_length - special_tokens_count)]
                     __lowerCamelCase     :      str       						=						label_ids[: (max_seq_length - special_tokens_count)]

               # The convention in BERT is:
               # (a) For sequence pairs:
               #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
               #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
               # (b) For single sequences:
               #  tokens:   [CLS] the dog is hairy . [SEP]
               #  type_ids:   0   0   0   0  0     0   0
               #
               # Where "type_ids" are used to indicate whether this is the first
               # sequence or the second sequence. The embedding vectors for `type=0` and
               # `type=1` were learned during pre-training and are added to the wordpiece
               # embedding vector (and position vector). This is not *strictly* necessary
               # since the [SEP] token unambiguously separates the sequences, but it makes
               # it easier for the model to learn the concept of sequences.
               #
               # For classification tasks, the first vector (corresponding to [CLS]) is
               # used as the "sentence vector". Note that this only makes sense because
               # the entire model is fine-tuned.
               tokens += [sep_token]
               label_ids += [pad_token_label_id]
               if sep_token_extra:
                     # roberta uses an extra separator b/w pairs of sentences
                     tokens += [sep_token]
                     label_ids += [pad_token_label_id]
               __lowerCamelCase     :      Any       						=						[sequence_a_segment_id] * len(_lowerCamelCase							)

               if cls_token_at_end:
                     tokens += [cls_token]
                     label_ids += [pad_token_label_id]
                     segment_ids += [cls_token_segment_id]
               else:
                     __lowerCamelCase     :      int       						=						[cls_token] + tokens
                     __lowerCamelCase     :      int       						=						[pad_token_label_id] + label_ids
                     __lowerCamelCase     :      Dict       						=						[cls_token_segment_id] + segment_ids

               __lowerCamelCase     :      Dict       						=						tokenizer.convert_tokens_to_ids(_lowerCamelCase							)

               # The mask has 1 for real tokens and 0 for padding tokens. Only real
               # tokens are attended to.
               __lowerCamelCase     :      Tuple       						=						[1 if mask_padding_with_zero else 0] * len(_lowerCamelCase							)

               # Zero-pad up to the sequence length.
               __lowerCamelCase     :      List[Any]       						=						max_seq_length - len(_lowerCamelCase							)
               if pad_on_left:
                     __lowerCamelCase     :      Optional[int]       						=						([pad_token] * padding_length) + input_ids
                     __lowerCamelCase     :      Optional[Any]       						=						([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                     __lowerCamelCase     :      Tuple       						=						([pad_token_segment_id] * padding_length) + segment_ids
                     __lowerCamelCase     :      Tuple       						=						([pad_token_label_id] * padding_length) + label_ids
               else:
                     input_ids += [pad_token] * padding_length
                     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                     segment_ids += [pad_token_segment_id] * padding_length
                     label_ids += [pad_token_label_id] * padding_length

               assert len(_lowerCamelCase							) == max_seq_length
               assert len(_lowerCamelCase							) == max_seq_length
               assert len(_lowerCamelCase							) == max_seq_length
               assert len(_lowerCamelCase							) == max_seq_length

               if ex_index < 5:
                     logger.info("""*** Example ***"""							)
                     logger.info("""guid: %s"""       ,  example.guid							)
                     logger.info("""tokens: %s"""       ,  """ """.join([str(_lowerCamelCase							) for x in tokens]							)							)
                     logger.info("""input_ids: %s"""       ,  """ """.join([str(_lowerCamelCase							) for x in input_ids]							)							)
                     logger.info("""input_mask: %s"""       ,  """ """.join([str(_lowerCamelCase							) for x in input_mask]							)							)
                     logger.info("""segment_ids: %s"""       ,  """ """.join([str(_lowerCamelCase							) for x in segment_ids]							)							)
                     logger.info("""label_ids: %s"""       ,  """ """.join([str(_lowerCamelCase							) for x in label_ids]							)							)

               if "token_type_ids" not in tokenizer.model_input_names:
                     __lowerCamelCase     :      str       						=						None

               features.append(
                   InputFeatures(
                       input_ids=_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  label_ids=_lowerCamelCase							)							)
         return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset







    class 							_UpperCamelCase (						A						):

       '''simple docstring'''







       a_				:							List[InputFeatures]
       a_				:							int    = nn.CrossEntropyLoss().ignore_index

       def __init__(  self  : Tuple       ,  _lowerCamelCase  : TokenClassificationTask       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : PreTrainedTokenizer       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[int] = None       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : Split = Split.train       ,  ):






             '''simple docstring'''



             __lowerCamelCase     :      List[Any]       						=						os.path.join(
                 _lowerCamelCase       ,  """cached_{}_{}_{}""".format(mode.value       ,  tokenizer.__class__.__name__       ,  str(_lowerCamelCase							)							)       ,  )

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             __lowerCamelCase     :      List[Any]       						=						cached_features_file + """.lock"""
             with FileLock(_lowerCamelCase							):
                   if os.path.exists(_lowerCamelCase							) and not overwrite_cache:
                         logger.info(F"""Loading features from cached file {cached_features_file}"""							)
                         __lowerCamelCase     :      Tuple       						=						torch.load(_lowerCamelCase							)
                   else:
                         logger.info(F"""Creating features from dataset file at {data_dir}"""							)
                         __lowerCamelCase     :      List[str]       						=						token_classification_task.read_examples_from_file(_lowerCamelCase       ,  _lowerCamelCase							)
                         # TODO clean up all this to leverage built-in features of tokenizers
                         __lowerCamelCase     :      List[str]       						=						token_classification_task.convert_examples_to_features(
                             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  cls_token_at_end=bool(model_type in ["""xlnet"""]							)       ,  cls_token=tokenizer.cls_token       ,  cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0       ,  sep_token=tokenizer.sep_token       ,  sep_token_extra=_lowerCamelCase       ,  pad_on_left=bool(tokenizer.padding_side == """left"""							)       ,  pad_token=tokenizer.pad_token_id       ,  pad_token_segment_id=tokenizer.pad_token_type_id       ,  pad_token_label_id=self.pad_token_label_id       ,  )
                         logger.info(F"""Saving features into cached file {cached_features_file}"""							)
                         torch.save(self.features       ,  _lowerCamelCase							)

       def __len__(  self  : Optional[int]							):






             '''simple docstring'''



             return len(self.features							)


       def __getitem__(  self  : str       ,  _lowerCamelCase  : Union[str, Any]							):






             '''simple docstring'''



             return self.features[i]


if is_tf_available():
    import tensorflow as tf







    class 							_UpperCamelCase :

       '''simple docstring'''







       a_				:							List[InputFeatures]
       a_				:							int    = -100

       def __init__(  self  : Optional[Any]       ,  _lowerCamelCase  : TokenClassificationTask       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : PreTrainedTokenizer       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[int] = None       ,  _lowerCamelCase  : int=False       ,  _lowerCamelCase  : Split = Split.train       ,  ):






             '''simple docstring'''



             __lowerCamelCase     :      Tuple       						=						token_classification_task.read_examples_from_file(_lowerCamelCase       ,  _lowerCamelCase							)
             # TODO clean up all this to leverage built-in features of tokenizers
             __lowerCamelCase     :      int       						=						token_classification_task.convert_examples_to_features(
                 _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  cls_token_at_end=bool(model_type in ["""xlnet"""]							)       ,  cls_token=tokenizer.cls_token       ,  cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0       ,  sep_token=tokenizer.sep_token       ,  sep_token_extra=_lowerCamelCase       ,  pad_on_left=bool(tokenizer.padding_side == """left"""							)       ,  pad_token=tokenizer.pad_token_id       ,  pad_token_segment_id=tokenizer.pad_token_type_id       ,  pad_token_label_id=self.pad_token_label_id       ,  )

             def gen():
                   for ex in self.features:
                         if ex.token_type_ids is None:
                               yield (
                                   {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                   ex.label_ids,
                               )
                         else:
                               yield (
                                   {
                                       "input_ids": ex.input_ids,
                                       "attention_mask": ex.attention_mask,
                                       "token_type_ids": ex.token_type_ids,
                                   },
                                   ex.label_ids,
                               )

             if "token_type_ids" not in tokenizer.model_input_names:
                   __lowerCamelCase     :      Any       						=						tf.data.Dataset.from_generator(
                       _lowerCamelCase       ,  ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)       ,  (
                           {"""input_ids""": tf.TensorShape([None]							), """attention_mask""": tf.TensorShape([None]							)},
                           tf.TensorShape([None]							),
                       )       ,  )
             else:
                   __lowerCamelCase     :      int       						=						tf.data.Dataset.from_generator(
                       _lowerCamelCase       ,  ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)       ,  (
                           {
                               """input_ids""": tf.TensorShape([None]							),
                               """attention_mask""": tf.TensorShape([None]							),
                               """token_type_ids""": tf.TensorShape([None]							),
                           },
                           tf.TensorShape([None]							),
                       )       ,  )

       def   _snake_case    (  self  : str							):






             '''simple docstring'''



             __lowerCamelCase     :      Union[str, Any]       						=						self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features							)							)							)

             return self.dataset

       def __len__(  self  : str							):






             '''simple docstring'''



             return len(self.features							)


       def __getitem__(  self  : List[Any]       ,  _lowerCamelCase  : Optional[Any]							):






             '''simple docstring'''



             return self.features[i]







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



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

   '''simple docstring'''







   a_				:							List[Any]    = ReformerTokenizer
   a_				:							Optional[int]    = ReformerTokenizerFast
   a_				:							Optional[int]    = True
   a_				:							List[Any]    = False
   a_				:							Any    = True

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         super().setUp()

         __lowerCamelCase     :      str       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)
         tokenizer.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						"""<s>"""
         __lowerCamelCase     :      Any       						=						1

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

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						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(_lowerCamelCase							)       ,  1_0_0_0							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size       ,  1_0_0_0							)

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         if not self.test_rust_tokenizer:
               return

         __lowerCamelCase     :      Dict       						=						self.get_tokenizer()
         __lowerCamelCase     :      Optional[int]       						=						self.get_rust_tokenizer()

         __lowerCamelCase     :      Optional[Any]       						=						"""I was born in 92000, and this is falsé."""

         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.tokenize(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						rust_tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						rust_tokenizer.encode(_lowerCamelCase       ,  add_special_tokens=_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.get_rust_tokenizer()
         __lowerCamelCase     :      Dict       						=						tokenizer.encode(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						rust_tokenizer.encode(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : int=1_5							):






         '''simple docstring'''



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                     __lowerCamelCase     :      Union[str, Any]       						=						self.rust_tokenizer_class.from_pretrained(_lowerCamelCase       ,  **_lowerCamelCase							)

                     # Simple input
                     __lowerCamelCase     :      Any       						=						"""This is a simple input"""
                     __lowerCamelCase     :      Optional[Any]       						=						["""This is a simple input 1""", """This is a simple input 2"""]
                     __lowerCamelCase     :      Union[str, Any]       						=						("""This is a simple input""", """This is a pair""")
                     __lowerCamelCase     :      Optional[int]       						=						[
                         ("""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(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Simple input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(_lowerCamelCase       ,  tokenizer_r.encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""							)

                     # Pair input
                     self.assertRaises(
                         _lowerCamelCase       ,  tokenizer_r.batch_encode_plus       ,  _lowerCamelCase       ,  max_length=_lowerCamelCase       ,  padding="""max_length"""       ,  )

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ReformerTokenizer(_lowerCamelCase       ,  keep_accents=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""This is a test"""							)
         self.assertListEqual(_lowerCamelCase       ,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCamelCase							)       ,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]       ,  )

         __lowerCamelCase     :      List[Any]       						=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 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     :      Union[str, Any]       						=						tokenizer.convert_tokens_to_ids(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]       ,  )

         __lowerCamelCase     :      List[str]       						=						tokenizer.convert_ids_to_tokens(_lowerCamelCase							)
         self.assertListEqual(
             _lowerCamelCase       ,  [
                 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  : List[str]							):






         '''simple docstring'''



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

   @slow
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						"""Hello World!"""
         __lowerCamelCase     :      Any       						=						[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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

   @slow
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      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"""
         )
         __lowerCamelCase     :      List[str]       						=						[
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

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

   @require_torch
   @slow
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         __lowerCamelCase     :      int       						=						list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
         __lowerCamelCase     :      Union[str, Any]       						=						""" """.join(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.encode_plus(_lowerCamelCase       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						self.big_tokenizer.batch_encode_plus([sequence, sequence]       ,  return_tensors="""pt"""							)

         __lowerCamelCase     :      List[Any]       						=						ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         __lowerCamelCase     :      List[str]       						=						encoded_sequence["""input_ids"""].shape
         __lowerCamelCase     :      Optional[Any]       						=						ReformerModel(_lowerCamelCase							)

         # 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(**_lowerCamelCase							)
               model(**_lowerCamelCase							)


   @slow
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						{"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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     :      int       						=						[
             """This is a very simple sentence.""",
             """The quick brown fox jumps over the lazy dog.""",
         ]

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



