

from typing import TYPE_CHECKING

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


__a						:Tuple            = {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a						:Optional[Any]            = ['LEDTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a						:List[Any]            = [
          'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LEDForConditionalGeneration',
          'LEDForQuestionAnswering',
          'LEDForSequenceClassification',
          'LEDModel',
          'LEDPreTrainedModel',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a						:Tuple            = ['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
      from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
      from .tokenization_led import LEDTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_led_fast import LEDTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_led import (
                LED_PRETRAINED_MODEL_ARCHIVE_LIST,
                LEDForConditionalGeneration,
                LEDForQuestionAnswering,
                LEDForSequenceClassification,
                LEDModel,
                LEDPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
      import sys

      __a						:str            = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"							,[
        (Version("1.0.0"						), True),
        ("1.0.0", True),
        (Version("2.0.0"						), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]							,)
def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							Version("1.0.0"						)
      assert (version == other) is expected_equality
      assert (version != other) is not expected_equality
      assert (hash(__UpperCamelCase						) == hash(__UpperCamelCase						)) is expected_equality