


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A      :    Optional[Any]		=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    Optional[int]		=					["FNetTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    List[Any]		=					["FNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    List[Any]		=					[
        "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "FNetForMaskedLM",
        "FNetForMultipleChoice",
        "FNetForNextSentencePrediction",
        "FNetForPreTraining",
        "FNetForQuestionAnswering",
        "FNetForSequenceClassification",
        "FNetForTokenClassification",
        "FNetLayer",
        "FNetModel",
        "FNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, 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 (
            FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            FNetForMaskedLM,
            FNetForMultipleChoice,
            FNetForNextSentencePrediction,
            FNetForPreTraining,
            FNetForQuestionAnswering,
            FNetForSequenceClassification,
            FNetForTokenClassification,
            FNetLayer,
            FNetModel,
            FNetPreTrainedModel,
        )


else:
    import sys

    A      :    str		=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


