from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE:    str     						=							{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Union[str, 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:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							[
			    '''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

			_SCREAMING_SNAKE_CASE:    List[str]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''SamImageProcessor''']


if TYPE_CHECKING:
			from .configuration_sam import (
			    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SamConfig,
			    SamMaskDecoderConfig,
			    SamPromptEncoderConfig,
			    SamVisionConfig,
			)
			from .processing_sam import SamProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_sam import SamImageProcessor

else:
			import sys

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

