import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= CpmAntTokenizer
  lowerCAmelCase__		:	str    		= False






  def 	_UpperCAmelCase				(				self:			str   ) -> Union[str, Any]:




    '''simple docstring'''




    super().setUp()

    __UpperCAmelCase						    =       [
        "<d>",
        "</d>",
        "<s>",
        "</s>",
        "</_>",
        "<unk>",
        "<pad>",
        "</n>",
        "我",
        "是",
        "C",
        "P",
        "M",
        "A",
        "n",
        "t",
    ]
    __UpperCAmelCase						    =       os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]   )
    with open(self.vocab_file				,				"w"				,				encoding="utf-8"   ) as vocab_writer:
      vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"   )
    __UpperCAmelCase						    =       "今天天气真好！"
    __UpperCAmelCase						    =       ["今天", "天气", "真", "好", "！"]
    __UpperCAmelCase						    =       tokenizer.tokenize(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       "今天天气真好！"
    __UpperCAmelCase						    =       [tokenizer.bos_token] + tokens

    __UpperCAmelCase						    =       [6, 9_802, 14_962, 2_082, 831, 244]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

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



