code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _lowerCamelCase							:		Union[str, Any]										=						{
      'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
      'tokenization_layoutlm': ['LayoutLMTokenizer'],
  }
  
  try:
  						if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						_lowerCamelCase							:		Tuple										=						['LayoutLMTokenizerFast']
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						_lowerCamelCase							:		Optional[int]										=						[
  						    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
  						    'LayoutLMForMaskedLM',
  						    'LayoutLMForSequenceClassification',
  						    'LayoutLMForTokenClassification',
  						    'LayoutLMForQuestionAnswering',
  						    'LayoutLMModel',
  						    'LayoutLMPreTrainedModel',
  						]
  
  try:
  						if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						_lowerCamelCase							:		Optional[Any]										=						[
  						    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
  						    'TFLayoutLMForMaskedLM',
  						    'TFLayoutLMForSequenceClassification',
  						    'TFLayoutLMForTokenClassification',
  						    'TFLayoutLMForQuestionAnswering',
  						    'TFLayoutLMMainLayer',
  						    'TFLayoutLMModel',
  						    'TFLayoutLMPreTrainedModel',
  						]
  
  
  if TYPE_CHECKING:
  						from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  						from .tokenization_layoutlm import LayoutLMTokenizer
  
  						try:
  												if not is_tokenizers_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_layoutlm import (
  												    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    LayoutLMForMaskedLM,
  												    LayoutLMForQuestionAnswering,
  												    LayoutLMForSequenceClassification,
  												    LayoutLMForTokenClassification,
  												    LayoutLMModel,
  												    LayoutLMPreTrainedModel,
  												)
  						try:
  												if not is_tf_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_tf_layoutlm import (
  												    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    TFLayoutLMForMaskedLM,
  												    TFLayoutLMForQuestionAnswering,
  												    TFLayoutLMForSequenceClassification,
  												    TFLayoutLMForTokenClassification,
  												    TFLayoutLMMainLayer,
  												    TFLayoutLMModel,
  												    TFLayoutLMPreTrainedModel,
  												)
  
  else:
  						import sys
  
  						_lowerCamelCase							:		int										=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
      VOCAB_FILES_NAMES,
      GPTNeoXJapaneseTokenizer,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				GPTNeoXJapaneseTokenizer
  					lowercase__					=				False
  					lowercase__					=				{"""do_clean_text""": False, """add_prefix_space""": False}
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							super().setUp()
  
  							_snake_case			 = [
  							    "こん",
  							    "こんに",
  							    "にちは",
  							    "ばんは",
  							    "世界,㔺界",
  							    "、",
  							    "。",
  							    "<BR>",
  							    "<SP>",
  							    "<TAB>",
  							    "<URL>",
  							    "<EMAIL>",
  							    "<TEL>",
  							    "<DATE>",
  							    "<PRICE>",
  							    "<BLOCK>",
  							    "<KIGOU>",
  							    "<U2000U2BFF>",
  							    "<|emoji1|>",
  							    "<unk>",
  							    "<|startoftext|>",
  							    "<|endoftext|>",
  							]
  							_snake_case			 = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
  							_snake_case			 = {"unk_token": "<unk>"}
  
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["vocab_file"]				)
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["emoji_file"]				)
  							with open(self.vocab_file,						"w",						encoding="utf-8"				) as vocab_writer:
  									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)
  							with open(self.emoji_file,						"w"				) as emoji_writer:
  									emoji_writer.write(json.dumps(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						**__UpperCamelCase				):
  							kwargs.update(self.special_tokens_map				)
  							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname,						**__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、㔺界。😀"
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、世界。😀"
  							return input_text, output_text
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case ,					_snake_case			 = self.get_input_output_texts(__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode(__UpperCamelCase,						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.decode(__UpperCamelCase,						clean_up_tokenization_spaces=__UpperCamelCase				)
  							return text, ids
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.get_tokenizer()
  
  							# Testing tokenization
  							_snake_case			 = "こんにちは、世界。　こんばんは、㔺界。"
  							_snake_case			 = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
  							_snake_case			 = tokenizer.tokenize(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids without special tokens
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids with special tokens
  							_snake_case			 = tokens + [tokenizer.unk_token]
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"				)
  
  							_snake_case			 = tokenizer.encode("ありがとう。",						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode("どういたしまして。",						add_special_tokens=__UpperCamelCase				)
  
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase				)
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase,						__UpperCamelCase				)
  
  							assert encoded_sentence == ids_a
  							assert encoded_pair == ids_a + ids_a
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# Intentionally convert some words to accommodate character fluctuations unique to Japanese
  							pass
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# tokenizer has no padding token
  							pass
  
  
  
  
  
  