





"""simple docstring"""



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							unittest.TestCase  ):





 """simple docstring"""


 _lowerCamelCase				 =  HerbertTokenizer
 _lowerCamelCase				 =  HerbertTokenizerFast
 _lowerCamelCase				 =  True

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     super().setUp()

     # Use a simpler test file without japanese/chinese characters
     with open(F"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""      ,		encoding='''utf-8'''						) as f_data:
         __A					:		Optional[int]						=  f_data.read().replace('''\n\n'''      ,		'''\n'''						).strip()

     __A					:		int						=  [
         '''<s>''',
         '''</s>''',
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         ''',</w>''',
         '''<unk>''',
     ]
     __A					:		Optional[Any]						=  dict(zip(__lowerCamelCase      ,		range(len(__lowerCamelCase						)						)						)						)
     __A					:		Tuple						=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A					:		List[Any]						=  os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''vocab_file''']						)
     __A					:		str						=  os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''merges_file''']						)
     with open(self.vocab_file      ,		'''w'''						) as fp:
         fp.write(json.dumps(__lowerCamelCase						)						)
     with open(self.merges_file      ,		'''w'''						) as fp:
         fp.write('''\n'''.join(__lowerCamelCase						)						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		int						=  '''lower newer'''
     __A					:		Optional[int]						=  '''lower newer'''
     return input_text, output_text

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		str						=  self.tokenizer_class(vocab_file=self.vocab_file      ,		merges_file=self.merges_file						)

     __A					:		Optional[Any]						=  '''lower'''
     __A					:		int						=  ['''low''', '''er</w>''']
     __A					:		Any						=  tokenizer.tokenize(__lowerCamelCase						)
     self.assertListEqual(__lowerCamelCase      ,		__lowerCamelCase						)

     __A					:		Dict						=  tokens + ['''<unk>''']
     __A					:		List[str]						=  [16, 17, 23]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase						)      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

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

     __A					:		Tuple						=  '''lower,newer'''

     __A					:		List[Any]						=  tokenizer.tokenize(__lowerCamelCase						)
     __A					:		Tuple						=  rust_tokenizer.tokenize(__lowerCamelCase						)
     self.assertListEqual(__lowerCamelCase      ,		__lowerCamelCase						)

     __A					:		Union[str, Any]						=  tokenizer.encode(__lowerCamelCase      ,		add_special_tokens=__lowerCamelCase						)
     __A					:		Optional[Any]						=  rust_tokenizer.encode(__lowerCamelCase      ,		add_special_tokens=__lowerCamelCase						)
     self.assertListEqual(__lowerCamelCase      ,		__lowerCamelCase						)

     __A					:		List[Any]						=  self.get_rust_tokenizer()
     __A					:		Union[str, Any]						=  tokenizer.encode(__lowerCamelCase						)
     __A					:		Optional[int]						=  rust_tokenizer.encode(__lowerCamelCase						)
     self.assertListEqual(__lowerCamelCase      ,		__lowerCamelCase						)







 @slow
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''						)

     __A					:		Dict						=  tokenizer.encode('''konstruowanie sekwencji'''      ,		add_special_tokens=__lowerCamelCase						)
     __A					:		Union[str, Any]						=  tokenizer.encode('''konstruowanie wielu sekwencji'''      ,		add_special_tokens=__lowerCamelCase						)

     __A					:		Any						=  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase						)
     __A					:		Optional[Any]						=  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase      ,		__lowerCamelCase						)

     assert encoded_sentence == [0] + text + [2]
     assert encoded_pair == [0] + text + [2] + text_a + [2]











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    ["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """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

       a_				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




