"""
    #################################################################################################################################################################################################
    # 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_tf_available, is_torch_available
_import_structure = {'configuration_tapas': ['TapasConfig'], 'tokenization_tapas': ['TapasTokenizer']}
try:
    if not is_torch_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_tapas"] = ["TapasForMaskedLM", "TapasForQuestionAnswering", "TapasForSequenceClassification", "TapasModel", "TapasPreTrainedModel", "load_tf_weights_in_tapas"]
try:
    if not is_tf_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_tf_tapas"] = ["TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTapasPreTrainedModel"]
if TYPE_CHECKING:
    from .configuration_tapas import TapasConfig
    from .tokenization_tapas import TapasTokenizer
    try:
        if not is_torch_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_tapas import (TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas)
    try:
        if not is_tf_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_tf_tapas import (TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel)
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.                                                                 #
    #################################################################################################################################################################################################
"""
