






from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Tuple	  =						["""PerceiverFeatureExtractor"""]
							__UpperCamelCase					:		Any	  =						["""PerceiverImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PerceiverForImageClassificationConvProcessing""",
							    """PerceiverForImageClassificationFourier""",
							    """PerceiverForImageClassificationLearned""",
							    """PerceiverForMaskedLM""",
							    """PerceiverForMultimodalAutoencoding""",
							    """PerceiverForOpticalFlow""",
							    """PerceiverForSequenceClassification""",
							    """PerceiverLayer""",
							    """PerceiverModel""",
							    """PerceiverPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
							from .tokenization_perceiver import PerceiverTokenizer

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_perceiver import PerceiverFeatureExtractor
														from .image_processing_perceiver import PerceiverImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_perceiver import (
														    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PerceiverForImageClassificationConvProcessing,
														    PerceiverForImageClassificationFourier,
														    PerceiverForImageClassificationLearned,
														    PerceiverForMaskedLM,
														    PerceiverForMultimodalAutoencoding,
														    PerceiverForOpticalFlow,
														    PerceiverForSequenceClassification,
														    PerceiverLayer,
														    PerceiverModel,
														    PerceiverPreTrainedModel,
														)

else:
							import sys

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












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




