'''simple docstring'''





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						OpenAIGPTTokenizer
    snake_case__							:   Optional[int]							=						OpenAIGPTTokenizerFast
    snake_case__							:   int							=						True
    snake_case__							:   str							=						False






    def       _UpperCamelCase    (       self )							-> int:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [
             '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>',
             '<unk>',
         ]
         SCREAMING_SNAKE_CASE					:			Any    	=       dict(zip(lowercase__    ,							range(len(lowercase__ ) ) ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         SCREAMING_SNAKE_CASE					:			Dict    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['vocab_file'] )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['merges_file'] )
         with open(self.vocab_file    ,							'w' ) as fp:
              fp.write(json.dumps(lowercase__ ) )
         with open(self.merges_file    ,							'w' ) as fp:
              fp.write('\n'.join(lowercase__ ) )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         return "lower newer", "lower newer"






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       OpenAIGPTTokenizer(self.vocab_file    ,							self.merges_file )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       'lower'
         SCREAMING_SNAKE_CASE					:			str    	=       ['low', 'er</w>']
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.tokenize(lowercase__ )
         self.assertListEqual(lowercase__    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokens + ['<unk>']
         SCREAMING_SNAKE_CASE					:			List[str]    	=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ )    ,							lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__=15 )							-> int:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                   SCREAMING_SNAKE_CASE					:			str    	=       self.rust_tokenizer_class.from_pretrained(lowercase__    ,							**lowercase__ )

                   # Simple input
                   SCREAMING_SNAKE_CASE					:			str    	=       'This is a simple input'
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       ['This is a simple input 1', 'This is a simple input 2']
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ('This is a simple input', 'This is a pair')
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                       ('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(lowercase__    ,							tokenizer_r.encode    ,							lowercase__    ,							max_length=lowercase__    ,							padding='max_length' )

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

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

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

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

                   # Pair input
                   self.assertRaises(
                       lowercase__    ,							tokenizer_r.batch_encode_plus    ,							lowercase__    ,							max_length=lowercase__    ,							padding='max_length'    ,							)






    def       _UpperCamelCase    (       self )							-> str:
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    pass




'''simple docstring'''





from typing import TYPE_CHECKING

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


_lowerCAmelCase  :int  				=    {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :str  				=    ["""AlbertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    ["""AlbertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :List[str]  				=    [
     """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """AlbertForMaskedLM""",
     """AlbertForMultipleChoice""",
     """AlbertForPreTraining""",
     """AlbertForQuestionAnswering""",
     """AlbertForSequenceClassification""",
     """AlbertForTokenClassification""",
     """AlbertModel""",
     """AlbertPreTrainedModel""",
     """load_tf_weights_in_albert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Any  				=    [
     """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFAlbertForMaskedLM""",
     """TFAlbertForMultipleChoice""",
     """TFAlbertForPreTraining""",
     """TFAlbertForQuestionAnswering""",
     """TFAlbertForSequenceClassification""",
     """TFAlbertForTokenClassification""",
     """TFAlbertMainLayer""",
     """TFAlbertModel""",
     """TFAlbertPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    [
     """FlaxAlbertForMaskedLM""",
     """FlaxAlbertForMultipleChoice""",
     """FlaxAlbertForPreTraining""",
     """FlaxAlbertForQuestionAnswering""",
     """FlaxAlbertForSequenceClassification""",
     """FlaxAlbertForTokenClassification""",
     """FlaxAlbertModel""",
     """FlaxAlbertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

 _lowerCAmelCase  :Union[str, Any]  				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



