"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import TYPE_CHECKING
from ...utils import (OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available)
_import_structure = {'configuration_mpnet': ['MPNetConfig'], 'tokenization_mpnet': ['MPNetTokenizer']}
try:
    if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_mpnet_fast"] = ["MPNetTokenizerFast"]
try:
    if not is_torch_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else:
    _import_structure["modeling_mpnet"] = ["MPNetForMaskedLM", "MPNetForMultipleChoice", "MPNetForQuestionAnswering", "MPNetForSequenceClassification", "MPNetForTokenClassification",
    "MPNetLayer", "MPNetModel", "MPNetPreTrainedModel"]
try:
    if not is_tf_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else:
    _import_structure["modeling_tf_mpnet"] = ["TFMPNetEmbeddings", "TFMPNetForMaskedLM", "TFMPNetForMultipleChoice", "TFMPNetForQuestionAnswering", "TFMPNetForSequenceClassification",
    "TFMPNetForTokenClassification", "TFMPNetMainLayer", "TFMPNetModel", "TFMPNetPreTrainedModel"]
if TYPE_CHECKING:
    from .configuration_mpnet import 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 (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 (TFMPNetEmbeddings, TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification,
        TFMPNetForTokenClassification, TFMPNetMainLayer, TFMPNetModel, TFMPNetPreTrainedModel)
else:
    import sys
    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
