



'''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

UpperCamelCase__:       Dict							     =      [(1,)]


def 			snake_case_					( )				->       Optional[Any]:
							UpperCAmelCase			:	Union[str, Any]										=		bool(os.getenv('''RUN_SLOW'''       ,				0  )  )
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"


def 			snake_case_					( )				->       Optional[int]:
							UpperCAmelCase			:	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     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	@slow
	@require_torch_gpu
	def 					A     (						self      :					Tuple		)      ->		Tuple:
								check_slow_torch_cuda()







	@require_torch_gpu
	@slow
	def 					A     (						self      :					Optional[Any]		)      ->		int:
								check_slow_torch_cuda()







	@parameterized.expand(lowerCamelCase__		)
	@slow
	def 					A     (						self      :					Optional[int]		,      __snake_case      :					List[Any]=None		)      ->		Optional[int]:
								check_slow()


@slow
@require_torch_gpu
def 			snake_case_					( )				->       Union[str, Any]:
							check_slow_torch_cuda()


@require_torch_gpu
@slow
def 			snake_case_					( )				->       Optional[int]:
							check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('''param'''       ,				[1]  )
def 			snake_case_					( _lowerCAmelCase  :						Optional[int]  )				->       Dict:
							check_slow()




@pytest.mark.parametrize('''param'''       ,				[1]  )
@slow
def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       str:
							check_slow()










'''simple docstring'''






from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       Any							     =      [
						    "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

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





