

def 						UpperCamelCase (lowercase_:			int )					->				list:

	# bit count represents no. of bits in the gray code
	if bit_count < 0:
		raise ValueError("""The given input must be positive""" )

	# get the generated string sequence
	A__							:   Optional[int]					       =		gray_code_sequence_string(lowercase_ )
	#
	# convert them to integers
	for i in range(len(lowercase_ ) ):
		A__							:   str					       =		int(sequence[i]   ,				2 )

	return sequence

def 						UpperCamelCase (lowercase_:			int )					->				list:

	# The approach is a recursive one
	# Base case achieved when either n = 0 or n=1
	if bit_count == 0:
		return ["0"]

	if bit_count == 1:
		return ["0", "1"]

	A__							:   Dict					       =		1 << bit_count  # defines the length of the sequence
	# 1<< n is equivalent to 2^n

	# recursive answer will generate answer for n-1 bits
	A__							:   int					       =		gray_code_sequence_string(bit_count - 1 )

	A__							:   Tuple					       =		[]

	# append 0 to first half of the smaller sequence generated
	for i in range(seq_len // 2 ):
		A__							:   int					       =		"""0""" + smaller_sequence[i]
		sequence.append(lowercase_ )

	# append 1 to second half ... start from the end of the list
	for i in reversed(range(seq_len // 2 ) ):
		A__							:   List[Any]					       =		"""1""" + smaller_sequence[i]
		sequence.append(lowercase_ )

	return sequence


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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



