

from __future__ import annotations





def 	__a     (      A__			:  list			,  A__			:  int			,  A__			:  int			,  A__			:  int       ):
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   input_list[low:mid], input_list[mid : high + 1]
    while left and right:
        result.append((left if left[0] <= right[0] else right).pop(0       )       )
    SCREAMING_SNAKE_CASE										=   result + left + right
    return input_list





def 	__a     (      A__			:  list       ):
    if len(A__       ) <= 1:
        return input_list
    SCREAMING_SNAKE_CASE										=   list(A__       )

    # iteration for two-way merging
    SCREAMING_SNAKE_CASE										=   2
    while p <= len(A__       ):
        # getting low, high and middle value for merge-sort of single list
        for i in range(0			,  len(A__       )			,  A__       ):
            SCREAMING_SNAKE_CASE										=   i
            SCREAMING_SNAKE_CASE										=   i + p - 1
            SCREAMING_SNAKE_CASE										=   (low + high + 1) // 2
            SCREAMING_SNAKE_CASE										=   merge(A__			,  A__			,  A__			,  A__       )
        # final merge of last two parts
        if p * 2 >= len(A__       ):
            SCREAMING_SNAKE_CASE										=   i
            SCREAMING_SNAKE_CASE										=   merge(A__			,  0			,  A__			,  len(A__       ) - 1       )
            break
        p *= 2

    return input_list


if __name__ == "__main__":
    __A			:					Any						=			input('Enter numbers separated by a comma:\n').strip()
    if user_input == "":
        __A			:					Union[str, Any]						=			[]
    else:
        __A			:					Optional[Any]						=			[int(item.strip()) for item in user_input.split(',')]
    print(iter_merge_sort(unsorted))


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)