






"""simple docstring"""






def 					_A			(__a							) ->       list:
    """simple docstring"""






    if len(__a							) < 2:
        return collection

    def circle_sort_util(__a    ,				__a    ,				__a							) -> bool:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		False

        if low == high:
            return swapped

        SCREAMING_SNAKE_CASE_      :  Any    						=		low
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		high

        while left < right:
            if collection[left] > collection[right]:
                SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(
                    collection[right],
                    collection[left],
                )
                SCREAMING_SNAKE_CASE_      :  str    						=		True

            left += 1
            right -= 1

        if left == right and collection[left] > collection[right + 1]:
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(
                collection[right + 1],
                collection[left],
            )

            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		True

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		low + int((high - low) / 2							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		circle_sort_util(__a    ,				__a    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		circle_sort_util(__a    ,				mid + 1    ,				__a							)

        return swapped or left_swap or right_swap

    SCREAMING_SNAKE_CASE_      :  str    						=		True

    while is_not_sorted is True:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		circle_sort_util(__a    ,				0    ,				len(__a							) - 1							)

    return collection


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







"""simple docstring"""






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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)