



'''simple docstring'''




from typing import TYPE_CHECKING

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


a     :    List[Any]      						=					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FalconForCausalLM""",
							    """FalconModel""",
							    """FalconPreTrainedModel""",
							    """FalconForSequenceClassification""",
							    """FalconForTokenClassification""",
							    """FalconForQuestionAnswering""",
							]


if TYPE_CHECKING:
							from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_falcon import (
														    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FalconForCausalLM,
														    FalconForQuestionAnswering,
														    FalconForSequenceClassification,
														    FalconForTokenClassification,
														    FalconModel,
														    FalconPreTrainedModel,
														)


else:
							import sys

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






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

