

from typing import TYPE_CHECKING

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


snake_case_   :   List[Any]        =       {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Optional[Any]        =       ['BeitFeatureExtractor']
	snake_case_   :   Tuple        =       ['BeitImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Tuple        =       [
	    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BeitForImageClassification',
	    'BeitForMaskedImageModeling',
	    'BeitForSemanticSegmentation',
	    'BeitModel',
	    'BeitPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Union[str, Any]        =       [
	    'FlaxBeitForImageClassification',
	    'FlaxBeitForMaskedImageModeling',
	    'FlaxBeitModel',
	    'FlaxBeitPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_beit import BeitFeatureExtractor
		from .image_processing_beit import BeitImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_beit import (
		    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BeitForImageClassification,
		    BeitForMaskedImageModeling,
		    BeitForSemanticSegmentation,
		    BeitModel,
		    BeitPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_beit import (
		    FlaxBeitForImageClassification,
		    FlaxBeitForMaskedImageModeling,
		    FlaxBeitModel,
		    FlaxBeitPreTrainedModel,
		)


else:
	import sys

	snake_case_   :   List[Any]        =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""






from typing import TYPE_CHECKING

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


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

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


