

'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_							=					{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					["XLNetTokenizer"]

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "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:
							UpperCamelCase_							=					[
							    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFXLNetForMultipleChoice",
							    "TFXLNetForQuestionAnsweringSimple",
							    "TFXLNetForSequenceClassification",
							    "TFXLNetForTokenClassification",
							    "TFXLNetLMHeadModel",
							    "TFXLNetMainLayer",
							    "TFXLNetModel",
							    "TFXLNetPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, 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 (
														    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    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 (
														    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLNetForMultipleChoice,
														    TFXLNetForQuestionAnsweringSimple,
														    TFXLNetForSequenceClassification,
														    TFXLNetForTokenClassification,
														    TFXLNetLMHeadModel,
														    TFXLNetMainLayer,
														    TFXLNetModel,
														    TFXLNetPreTrainedModel,
														)

else:
							import sys

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








'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Union[str, Any]		,				*UpperCAmelCase__    :		Dict		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										warnings.warn(
										    '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
										    ''' use CLIPImageProcessor instead.'''		,				UpperCAmelCase__		,				)
										super().__init__(*UpperCAmelCase__		,				**UpperCAmelCase__      )





