



'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_				:					Any							=      {
    """configuration_ernie_m""": ["""ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieMConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_				:					List[str]							=      ["""ErnieMTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_				:					Optional[int]							=      [
       """ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ErnieMForMultipleChoice""",
       """ErnieMForQuestionAnswering""",
       """ErnieMForSequenceClassification""",
       """ErnieMForTokenClassification""",
       """ErnieMModel""",
       """ErnieMPreTrainedModel""",
       """ErnieMForInformationExtraction""",
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   A_				:					Dict							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''






from unittest.mock import patch

import datasets
from datasets import Dataset




def        snake_case_   (	)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}						)

       with patch("""tqdm.auto.tqdm"""						) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_called()



