


import random






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					a[left_index]
			SCREAMING_SNAKE_CASE_		:     Tuple					=					left_index + 1
			for j in range(left_index + 1,      A__				):
						if a[j] < pivot:
									SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					a[i], a[j]
									i += 1
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					a[i - 1], a[left_index]
			return i - 1






def 						a__			(				A__,      A__,      A__				):
			if left < right:
						SCREAMING_SNAKE_CASE_		:     int					=					random.randint(A__,      right - 1				)
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
						    a[left],
						    a[pivot],
						)  # switches the pivot with the left most bound
						SCREAMING_SNAKE_CASE_		:     int					=					partition(A__,      A__,      A__				)
						quick_sort_random(
						    A__,      A__,      A__				)  # recursive quicksort to the left of the pivot point
						quick_sort_random(
						    A__,      pivot_index + 1,      A__				)  # recursive quicksort to the right of the pivot point






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					input('Enter numbers separated by a comma:\n'				).strip()
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[int(A__				) for item in user_input.split(','				)]

			quick_sort_random(A__,      0,      len(A__				)				)

			print(A__				)


if __name__ == "__main__":
				main()













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      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



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

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

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

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





