
'''simple docstring'''
import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a_     =  [(1,)]

def   UpperCamelCase_       (		):
    """simple docstring"""






    snake_case_	:      Any            =  bool(os.getenv("RUN_SLOW",						0    )    )
    if run_slow:
        assert True
    else:
        assert False, "should have been skipped"

def   UpperCamelCase_       (		):
    """simple docstring"""






    snake_case_	:      List[Any]            =  bool(os.getenv("RUN_SLOW",						0    )    )
    if run_slow and torch_device == "cuda":
        assert True
    else:
        assert False, "should have been skipped"






@require_torch
class 				UpperCAmelCase_			(		unittest.TestCase   ):





 @slow
 @require_torch_gpu
 def     snake_case__						(      self):
     check_slow_torch_cuda()





 @require_torch_gpu
 @slow
 def     snake_case__						(      self):
     check_slow_torch_cuda()





 @parameterized.expand(lowercase_)
 @slow
 def     snake_case__						(      self	,    lowercase_=None):
     check_slow()


@slow
@require_torch_gpu
def   UpperCamelCase_       (		):
    """simple docstring"""






    check_slow_torch_cuda()

@require_torch_gpu
@slow
def   UpperCamelCase_       (		):
    """simple docstring"""






    check_slow_torch_cuda()

@slow
@pytest.mark.parametrize("param",						[1]    )
def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    check_slow()


@pytest.mark.parametrize("param",						[1]    )
@slow
def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    check_slow()






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

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




