
"""simple docstring"""


def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if number < 0 or shift_amount < 0:
						raise ValueError('''both inputs must be positive integers'''						)

			lowerCamelCase_					=  str(bin(lowerCAmelCase__						)						)
			binary_number += "0" * shift_amount
			return binary_number






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if number < 0 or shift_amount < 0:
						raise ValueError('''both inputs must be positive integers'''						)

			lowerCamelCase_					=  str(bin(lowerCAmelCase__						)						)[2:]
			if shift_amount >= len(lowerCAmelCase__						):
						return "0b0"
			lowerCamelCase_					=  binary_number[: len(lowerCAmelCase__						) - shift_amount]
			return "0b" + shifted_binary_number






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if number >= 0:  # Get binary representation of positive number
						lowerCamelCase_					=  '''0''' + str(bin(lowerCAmelCase__						)						).strip('''-'''						)[2:]
			else:  # Get binary (2's complement) representation of negative number
						lowerCamelCase_					=  len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
						lowerCamelCase_					=  bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
						lowerCamelCase_					=  (
						    '''1''' + '''0''' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
						)

			if shift_amount >= len(lowerCAmelCase__						):
						return "0b" + binary_number[0] * len(lowerCAmelCase__						)
			return (
			    "0b"
			    + binary_number[0] * shift_amount
			    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

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





