






"""simple docstring"""



from __future__ import annotations


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   list[int | float]							)->		int | float:






     '''simple docstring'''



     if len(snake_case							) == 0:
          raise ValueError("find_max() arg is an empty sequence"							)
     UpperCAmelCase__	:						Optional[Any]       =       nums[0]
     for x in nums:
          if x > max_num:
               UpperCAmelCase__	:						int       =       x
     return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)










"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase  :  List[Any]   =			{
    """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:
 _lowerCAmelCase  :  str   =			["""MarkupLMTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  List[str]   =			[
     """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

 _lowerCAmelCase  :  Dict   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


