from typing import TYPE_CHECKING

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


__lowerCAmelCase :Tuple     =    {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :int     =    ['RobertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Any     =    [
							    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'RobertaForCausalLM',
							    'RobertaForMaskedLM',
							    'RobertaForMultipleChoice',
							    'RobertaForQuestionAnswering',
							    'RobertaForSequenceClassification',
							    'RobertaForTokenClassification',
							    'RobertaModel',
							    'RobertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Union[str, Any]     =    [
							    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFRobertaForCausalLM',
							    'TFRobertaForMaskedLM',
							    'TFRobertaForMultipleChoice',
							    'TFRobertaForQuestionAnswering',
							    'TFRobertaForSequenceClassification',
							    'TFRobertaForTokenClassification',
							    'TFRobertaMainLayer',
							    'TFRobertaModel',
							    'TFRobertaPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Optional[int]     =    [
							    'FlaxRobertaForCausalLM',
							    'FlaxRobertaForMaskedLM',
							    'FlaxRobertaForMultipleChoice',
							    'FlaxRobertaForQuestionAnswering',
							    'FlaxRobertaForSequenceClassification',
							    'FlaxRobertaForTokenClassification',
							    'FlaxRobertaModel',
							    'FlaxRobertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
							from .tokenization_roberta import RobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roberta_fast import RobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta import (
														    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaForCausalLM,
														    RobertaForMaskedLM,
														    RobertaForMultipleChoice,
														    RobertaForQuestionAnswering,
														    RobertaForSequenceClassification,
														    RobertaForTokenClassification,
														    RobertaModel,
														    RobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta import (
														    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaForCausalLM,
														    TFRobertaForMaskedLM,
														    TFRobertaForMultipleChoice,
														    TFRobertaForQuestionAnswering,
														    TFRobertaForSequenceClassification,
														    TFRobertaForTokenClassification,
														    TFRobertaMainLayer,
														    TFRobertaModel,
														    TFRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta import (
														    FlaxRobertaForCausalLM,
														    FlaxRobertaForMaskedLM,
														    FlaxRobertaForMultipleChoice,
														    FlaxRobertaForQuestionAnswering,
														    FlaxRobertaForSequenceClassification,
														    FlaxRobertaForTokenClassification,
														    FlaxRobertaModel,
														    FlaxRobertaPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase :Optional[int]     =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      None:


							'''simple docstring'''

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