

"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase    : int				 =						{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						['''BloomTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[str]				 =						[
							    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BloomForCausalLM''',
							    '''BloomModel''',
							    '''BloomPreTrainedModel''',
							    '''BloomForSequenceClassification''',
							    '''BloomForTokenClassification''',
							    '''BloomForQuestionAnswering''',
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

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







"""simple docstring"""





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

	lowerCAmelCase__      :		int    							=      str(bin(A_					)					)
	binary_number += "0" * shift_amount
	return binary_number

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

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()




