






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE								=  {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE								=  ["""XLMProphetNetTokenizer"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE								=  [
      """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLMProphetNetDecoder""",
      """XLMProphetNetEncoder""",
      """XLMProphetNetForCausalLM""",
      """XLMProphetNetForConditionalGeneration""",
      """XLMProphetNetModel""",
      """XLMProphetNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm_prophetnet import (
        XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMProphetNetDecoder,
        XLMProphetNetEncoder,
        XLMProphetNetForCausalLM,
        XLMProphetNetForConditionalGeneration,
        XLMProphetNetModel,
        XLMProphetNetPreTrainedModel,
    )

else:
  import sys

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












from unittest.mock import patch

import datasets
from datasets import Dataset

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Union[str, Any]     					=			Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}	)

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

        mock_tqdm.reset_mock()

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




