




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if number < 0 or shift_amount < 0:
						raise ValueError("""both inputs must be positive integers"""   )

			SCREAMING_SNAKE_CASE           =					str(bin(SCREAMING_SNAKE_CASE   )   )
			binary_number += "0" * shift_amount
			return binary_number
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if number < 0 or shift_amount < 0:
						raise ValueError("""both inputs must be positive integers"""   )

			SCREAMING_SNAKE_CASE           =					str(bin(SCREAMING_SNAKE_CASE   )   )[2:]
			if shift_amount >= len(SCREAMING_SNAKE_CASE   ):
						return "0b0"
			SCREAMING_SNAKE_CASE           =					binary_number[: len(SCREAMING_SNAKE_CASE   ) - shift_amount]
			return "0b" + shifted_binary_number
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if number >= 0:  # Get binary representation of positive number
						SCREAMING_SNAKE_CASE           =					"""0""" + str(bin(SCREAMING_SNAKE_CASE   )   ).strip("""-"""   )[2:]
			else:  # Get binary (2's complement) representation of negative number
						SCREAMING_SNAKE_CASE           =					len(bin(SCREAMING_SNAKE_CASE   )[3:]   )  # Find 2's complement of number
						SCREAMING_SNAKE_CASE           =					bin(abs(SCREAMING_SNAKE_CASE   ) - (1 << binary_number_length)   )[3:]
						SCREAMING_SNAKE_CASE           =					(
						    """1""" + """0""" * (binary_number_length - len(SCREAMING_SNAKE_CASE   )) + binary_number
						)

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






