





from typing import TYPE_CHECKING

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


UpperCAmelCase__    :      Union[str, Any]       					={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Union[str, Any]       					=['''MPNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Optional[Any]       					=[
							    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MPNetForMaskedLM''',
							    '''MPNetForMultipleChoice''',
							    '''MPNetForQuestionAnswering''',
							    '''MPNetForSequenceClassification''',
							    '''MPNetForTokenClassification''',
							    '''MPNetLayer''',
							    '''MPNetModel''',
							    '''MPNetPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      str       					=[
							    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFMPNetEmbeddings''',
							    '''TFMPNetForMaskedLM''',
							    '''TFMPNetForMultipleChoice''',
							    '''TFMPNetForQuestionAnswering''',
							    '''TFMPNetForSequenceClassification''',
							    '''TFMPNetForTokenClassification''',
							    '''TFMPNetMainLayer''',
							    '''TFMPNetModel''',
							    '''TFMPNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
							from .tokenization_mpnet import MPNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mpnet_fast import MPNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mpnet import (
														    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MPNetForMaskedLM,
														    MPNetForMultipleChoice,
														    MPNetForQuestionAnswering,
														    MPNetForSequenceClassification,
														    MPNetForTokenClassification,
														    MPNetLayer,
														    MPNetModel,
														    MPNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mpnet import (
														    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMPNetEmbeddings,
														    TFMPNetForMaskedLM,
														    TFMPNetForMultipleChoice,
														    TFMPNetForQuestionAnswering,
														    TFMPNetForSequenceClassification,
														    TFMPNetForTokenClassification,
														    TFMPNetMainLayer,
														    TFMPNetModel,
														    TFMPNetPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__    :      Dict       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
    '''processing_donut''': ['''DonutProcessor'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Any       					=[
							    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DonutSwinModel''',
							    '''DonutSwinPreTrainedModel''',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Tuple       					=['''DonutFeatureExtractor''']
							UpperCAmelCase__    :      List[str]       					=['''DonutImageProcessor''']


if TYPE_CHECKING:
							from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
							from .processing_donut import DonutProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_donut_swin import (
														    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DonutSwinModel,
														    DonutSwinPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_donut import DonutFeatureExtractor
														from .image_processing_donut import DonutImageProcessor

else:
							import sys

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





