"""
    #################################################################################################################################################################################################
    # 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_tokenizers_available, is_torch_available)
_import_structure = {"configuration_fnet": ["FNetConfig"]}
try:
    if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_fnet"] = ["FNetTokenizer"]
try:
    if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_fnet_fast"] = ["FNetTokenizerFast"]
try:
    if not is_torch_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else:
    _import_structure["modeling_fnet"] = ["FNetForMaskedLM", "FNetForMultipleChoice", "FNetForNextSentencePrediction", "FNetForPreTraining", "FNetForQuestionAnswering",
    "FNetForSequenceClassification", "FNetForTokenClassification", "FNetLayer", "FNetModel", "FNetPreTrainedModel"]
if TYPE_CHECKING:
    from .configuration_fnet import FNetConfig
    try:
        if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_fnet import FNetTokenizer
    try:
        if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_fnet_fast import FNetTokenizerFast
    try:
        if not is_torch_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else:
        from .modeling_fnet import (FNetForMaskedLM, FNetForMultipleChoice, FNetForNextSentencePrediction, FNetForPreTraining, FNetForQuestionAnswering,
        FNetForSequenceClassification, FNetForTokenClassification, FNetLayer, FNetModel, FNetPreTrainedModel)
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.                                                                 #
    #################################################################################################################################################################################################
"""
