


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[int]    							=					[]

       __UpperCamelCase							:							Optional[Any]    							=					os.path.join("tests", "models", "**", "test_modeling_*.py")
       __UpperCamelCase							:							Optional[Any]    							=					glob.glob(pattern)
       # TODO: deal with TF/Flax too
       __UpperCamelCase							:							Any    							=					[
           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:
              __UpperCamelCase							:							str    							=					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:
                            __UpperCamelCase							:							Optional[int]    							=					tester_class(parent=None)
                     except Exception:
                            continue
                     if hasattr(tester, "get_config"):
                            __UpperCamelCase							:							Dict    							=					tester.get_config()
                            for k, v in config.to_dict().items():
                                   if isinstance(v, int):
                                          __UpperCamelCase							:							Union[str, Any]    							=					None
                                          if k in ["vocab_size"]:
                                                 __UpperCamelCase							:							Dict    							=					100
                                          elif k in ["max_position_embeddings"]:
                                                 __UpperCamelCase							:							str    							=					128
                                          elif k in ["hidden_size", "d_model"]:
                                                 __UpperCamelCase							:							int    							=					40
                                          elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                                 __UpperCamelCase							:							Union[str, Any]    							=					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))






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							List[Any]    							=					{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					["MPNetTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					[
           "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MPNetForMaskedLM",
           "MPNetForMultipleChoice",
           "MPNetForQuestionAnswering",
           "MPNetForSequenceClassification",
           "MPNetForTokenClassification",
           "MPNetLayer",
           "MPNetModel",
           "MPNetPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							List[Any]    							=					[
           "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMPNetEmbeddings",
           "TFMPNetForMaskedLM",
           "TFMPNetForMultipleChoice",
           "TFMPNetForQuestionAnswering",
           "TFMPNetForSequenceClassification",
           "TFMPNetForTokenClassification",
           "TFMPNetMainLayer",
           "TFMPNetModel",
           "TFMPNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
       from .tokenization_mpnet import MPNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mpnet_fast import MPNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mpnet import (
                  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MPNetForMaskedLM,
                  MPNetForMultipleChoice,
                  MPNetForQuestionAnswering,
                  MPNetForSequenceClassification,
                  MPNetForTokenClassification,
                  MPNetLayer,
                  MPNetModel,
                  MPNetPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mpnet import (
                  TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMPNetEmbeddings,
                  TFMPNetForMaskedLM,
                  TFMPNetForMultipleChoice,
                  TFMPNetForQuestionAnswering,
                  TFMPNetForSequenceClassification,
                  TFMPNetForTokenClassification,
                  TFMPNetMainLayer,
                  TFMPNetModel,
                  TFMPNetPreTrainedModel,
              )

else:
       import sys

       __UpperCamelCase							:							Tuple    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


