"""simple docstring"""


from typing import TYPE_CHECKING

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


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

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase										=     [
        "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										=     [
        "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										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    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:
										snake_case_  :							Any            =	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:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	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))





