


from typing import TYPE_CHECKING

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


A   =			{
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   =			[
        "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LongformerForMaskedLM",
        "LongformerForMultipleChoice",
        "LongformerForQuestionAnswering",
        "LongformerForSequenceClassification",
        "LongformerForTokenClassification",
        "LongformerModel",
        "LongformerPreTrainedModel",
        "LongformerSelfAttention",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   =			[
        "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLongformerForMaskedLM",
        "TFLongformerForMultipleChoice",
        "TFLongformerForQuestionAnswering",
        "TFLongformerForSequenceClassification",
        "TFLongformerForTokenClassification",
        "TFLongformerModel",
        "TFLongformerPreTrainedModel",
        "TFLongformerSelfAttention",
    ]


if TYPE_CHECKING:
    from .configuration_longformer import (
        LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LongformerConfig,
        LongformerOnnxConfig,
    )
    from .tokenization_longformer import LongformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_longformer_fast import LongformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_longformer import (
            LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            LongformerForMaskedLM,
            LongformerForMultipleChoice,
            LongformerForQuestionAnswering,
            LongformerForSequenceClassification,
            LongformerForTokenClassification,
            LongformerModel,
            LongformerPreTrainedModel,
            LongformerSelfAttention,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_longformer import (
            TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLongformerForMaskedLM,
            TFLongformerForMultipleChoice,
            TFLongformerForQuestionAnswering,
            TFLongformerForSequenceClassification,
            TFLongformerForTokenClassification,
            TFLongformerModel,
            TFLongformerPreTrainedModel,
            TFLongformerSelfAttention,
        )

else:
    import sys

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












'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

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


