




'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase__								=						{
    '''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:
       lowercase__								=						['''MarkupLMTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__								=						[
           '''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

       lowercase__								=						_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

a__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






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




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						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 __magic_name__(						unittest.TestCase      ):






						@slow
						@require_torch_gpu
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




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





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()