





import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      __lowercase				    =      []

      __lowercase				    =      os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      __lowercase				    =      glob.glob(pattern)
      # TODO: deal with TF/Flax too
      __lowercase				    =      [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            __lowercase				    =      get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        __lowercase				    =      tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        __lowercase				    =      tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    __lowercase				    =      None
                                    if k in ["vocab_size"]:
                                          __lowercase				    =      100
                                    elif k in ["max_position_embeddings"]:
                                          __lowercase				    =      128
                                    elif k in ["hidden_size", "d_model"]:
                                          __lowercase				    =      40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          __lowercase				    =      5
                                    if target is not None and v > target:
                                          failures.append(
                                              f"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                                              f""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                                              f""" than {target}."""
                                          )

      if len(failures) > 0:
            raise Exception(f"""There were {len(failures)} failures:\n""" + """\n""".join(failures))

'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase       :     Tuple			=    {
    """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       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """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       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

