






def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						stooge(_A,   0,   len(_A			) - 1			)
						return arr

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







						if i >= h:
												return

						# If first element is smaller than the last then swap them
						if arr[i] > arr[h]:
												__magic_name__						,__magic_name__      :       Any       						= arr[h], arr[i]

						# If there are more than 2 elements in the array
						if h - i + 1 > 2:
												__magic_name__      :       Dict       						= (int)((h - i + 1) / 3			)

												# Recursively sort first 2/3 elements
												stooge(_A,   _A,   (h - t)			)

												# Recursively sort last 2/3 elements
												stooge(_A,   i + t,   (_A)			)

												# Recursively sort first 2/3 elements
												stooge(_A,   _A,   (h - t)			)


if __name__ == "__main__":
		__magic_name__:					List[Any]							=     input("Enter numbers separated by a comma:\n").strip()
		__magic_name__:					List[Any]							=     [int(item) for item in user_input.split(",")]
		print(stooge_sort(unsorted))








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

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