



from typing import TYPE_CHECKING

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


UpperCamelCase_	       =   {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MarkupLMForQuestionAnswering",
							    "MarkupLMForSequenceClassification",
							    "MarkupLMForTokenClassification",
							    "MarkupLMModel",
							    "MarkupLMPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)








'''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

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[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  _snake_case						(unittest.TestCase):






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






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






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


@require_torch_gpu
@slow
def       a__    (  )			-> List[str]:


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




