

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						__magic_name__			(							lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   MgpstrTokenizer
   lowerCamelCase__ :	Tuple    =   False
   lowerCamelCase__ :	Dict    =   {}
   lowerCamelCase__ :	int    =   False



   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     super().setUp()

     # fmt: off
     a__  =['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
     # fmt: on
     a__  =dict(zip(lowercase_,    range(len(lowercase_       )       )       )       )

     a__  =os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['''vocab_file''']       )
     with open(self.vocab_file,    '''w''',    encoding='''utf-8'''       ) as fp:
       fp.write(json.dumps(lowercase_       ) + '''\n'''       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Tuple:






     """simple docstring"""






     return MgpstrTokenizer.from_pretrained(self.tmpdirname,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  ='''tester'''
     a__  ='''tester'''
     return input_text, output_text



   @unittest.skip('''MGP-STR always lower cases letters.'''       )
   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     pass



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =self.get_tokenizers(do_lower_case=lowercase_       )
     for tokenizer in tokenizers:
       with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
         a__  ='''[SPECIAL_TOKEN]'''

         tokenizer.add_special_tokens({'''cls_token''': special_token}       )
         a__  =tokenizer.encode([special_token],    add_special_tokens=lowercase_       )
         self.assertEqual(len(lowercase_       ),    1       )

         a__  =tokenizer.decode(lowercase_,    skip_special_tokens=lowercase_       )
         self.assertTrue(special_token not in decoded       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.get_tokenizers()
     for tokenizer in tokenizers:
       with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
         a__,      a__  =self.get_input_output_texts(lowercase_       )

         a__  =tokenizer.tokenize(lowercase_       )
         a__  =tokenizer.convert_tokens_to_ids(lowercase_       )
         a__  =tokenizer.encode(lowercase_,    add_special_tokens=lowercase_       )
         self.assertListEqual(lowercase_,    lowercase_       )

         a__  =tokenizer.convert_ids_to_tokens(lowercase_       )
         self.assertNotEqual(len(lowercase_       ),    0       )
         a__  =tokenizer.decode(lowercase_       )
         self.assertIsInstance(lowercase_,    lowercase_       )

         self.assertEqual(text_a.replace(''' ''',    ''''''       ),    lowercase_       )



   @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     pass



   @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     pass



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''XLNetForMultipleChoice''',
           '''XLNetForQuestionAnswering''',
           '''XLNetForQuestionAnsweringSimple''',
           '''XLNetForSequenceClassification''',
           '''XLNetForTokenClassification''',
           '''XLNetLMHeadModel''',
           '''XLNetModel''',
           '''XLNetPreTrainedModel''',
           '''load_tf_weights_in_xlnet''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFXLNetForMultipleChoice''',
           '''TFXLNetForQuestionAnsweringSimple''',
           '''TFXLNetForSequenceClassification''',
           '''TFXLNetForTokenClassification''',
           '''TFXLNetLMHeadModel''',
           '''TFXLNetMainLayer''',
           '''TFXLNetModel''',
           '''TFXLNetPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet import XLNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet_fast import XLNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlnet import (
                  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLNetForMultipleChoice,
                  XLNetForQuestionAnswering,
                  XLNetForQuestionAnsweringSimple,
                  XLNetForSequenceClassification,
                  XLNetForTokenClassification,
                  XLNetLMHeadModel,
                  XLNetModel,
                  XLNetPreTrainedModel,
                  load_tf_weights_in_xlnet,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlnet import (
                  TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLNetForMultipleChoice,
                  TFXLNetForQuestionAnsweringSimple,
                  TFXLNetForSequenceClassification,
                  TFXLNetForTokenClassification,
                  TFXLNetLMHeadModel,
                  TFXLNetMainLayer,
                  TFXLNetModel,
                  TFXLNetPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
