"""
    #################################################################################################################################################################################################
    # 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_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available)
_import_structure = {"configuration_xlnet": ["XLNetConfig"]}
try:
    if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_xlnet"] = ["XLNetTokenizer"]
try:
    if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_xlnet_fast"] = ["XLNetTokenizerFast"]
try:
    if not is_torch_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_xlnet"] = ["XLNetForMultipleChoice", "XLNetForQuestionAnswering", "XLNetForQuestionAnsweringSimple", "XLNetForSequenceClassification",
"XLNetForTokenClassification", "XLNetLMHeadModel", "XLNetModel", "XLNetPreTrainedModel", "load_tf_weights_in_xlnet"]
try:
    if not is_tf_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_tf_xlnet"] = ["TFXLNetForMultipleChoice", "TFXLNetForQuestionAnsweringSimple", "TFXLNetForSequenceClassification", "TFXLNetForTokenClassification",
"TFXLNetLMHeadModel", "TFXLNetMainLayer", "TFXLNetModel", "TFXLNetPreTrainedModel"]
if TYPE_CHECKING:
    from .configuration_xlnet import XLNetConfig
    try:
        if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_xlnet import XLNetTokenizer
    try:
        if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_xlnet_fast import XLNetTokenizerFast
    try:
        if not is_torch_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_xlnet import (XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification,
    XLNetLMHeadModel, XLNetModel, XLNetPreTrainedModel, load_tf_weights_in_xlnet)
    try:
        if not is_tf_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_tf_xlnet import (TFXLNetForMultipleChoice, TFXLNetForQuestionAnsweringSimple, TFXLNetForSequenceClassification, TFXLNetForTokenClassification,
    TFXLNetLMHeadModel, TFXLNetMainLayer, TFXLNetModel, TFXLNetPreTrainedModel)
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.                                                                 #
    #################################################################################################################################################################################################
"""
