


def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				int						   =		len(__lowerCAmelCase		)
 for i in range(length - 1		):
  __lowerCamelCase						:				List[Any]						   =		i
  for k in range(i + 1     ,			__lowerCAmelCase		):
   if collection[k] < collection[least]:
    __lowerCamelCase						:				Any						   =		k
  if least != i:
   __lowerCamelCase   ,	__lowerCamelCase						:				Tuple						   =		(collection[i], collection[least])
 return collection


if __name__ == "__main__":
       lowercase_			       =      input('Enter numbers separated by a comma:\n').strip()
       lowercase_			       =      [int(item) for item in user_input.split(',')]
       print(selection_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 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

