





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			BioGptTokenizer
 lowercase_		=			False

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:      Union[str, Any]			  =[
       "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>",
   ]
   lowerCamelCase__:      Dict			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      Tuple			  =["l o 123", "lo w 1456", "e r</w> 1789", ""]

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   int)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  ="lower newer"
   lowerCamelCase__:      Any			  ="lower newer"
   return input_text, output_text

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =BioGptTokenizer(self.vocab_file       , self.merges_file)

   lowerCamelCase__:      Dict			  ="lower"
   lowerCamelCase__:      Dict			  =["low", "er</w>"]
   lowerCamelCase__:      Optional[int]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  =tokens + ["<unk>"]
   lowerCamelCase__:      Optional[int]			  =[14, 15, 20]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =BioGptTokenizer.from_pretrained("microsoft/biogpt")

   lowerCamelCase__:      Dict			  =tokenizer.encode("sequence builders"       , add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =tokenizer.encode("multi-sequence build"       , add_special_tokens=UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
   lowerCamelCase__:      int			  =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_       , UpperCAmelCase_)

   self.assertTrue(encoded_sentence == [2] + text)
   self.assertTrue(encoded_pair == [2] + text + [2] + text_a)








from typing import TYPE_CHECKING

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


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

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

