






from __future__ import annotations






def 				lowerCamelCase__      (    __lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Optional[Any]  ,		__lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Any           =  []
  __UpperCAmelCase							:	Any           =  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		)		)
  __UpperCAmelCase							:	str           =  result + left + right
  return input_list






def 				lowerCamelCase__      (    __lowerCamelCase			:     str		):





  if len(__a		) <= 1:
    return input_list
  __UpperCAmelCase							:	Any           =  list(__a		)

  # iteration for two-way merging
  __UpperCAmelCase							:	str           =  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		):
      __UpperCAmelCase							:	Dict           =  i
      __UpperCAmelCase							:	List[str]           =  i + p - 1
      __UpperCAmelCase							:	int           =  (low + high + 1) // 2
      __UpperCAmelCase							:	Optional[int]           =  merge(__a  ,		__a  ,		__a  ,		__a		)
    # final merge of last two parts
    if p * 2 >= len(__a		):
      __UpperCAmelCase							:	List[Any]           =  i
      __UpperCAmelCase							:	Optional[int]           =  merge(__a  ,		0  ,		__a  ,		len(__a		) - 1		)
      break
    p *= 2

  return input_list


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









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Dict=7       , UpperCAmelCase_				:   List[str]=3       , UpperCAmelCase_				:   str=30       , UpperCAmelCase_				:   List[str]=400       , UpperCAmelCase_				:   Optional[int]=True       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   Tuple=0.9       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   int=True       , UpperCAmelCase_				:   Union[str, Any]=[0.5, 0.5, 0.5]       , UpperCAmelCase_				:   Optional[Any]=[0.5, 0.5, 0.5]       , )       ->str:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =size if size is not None else {"shortest_edge": 30}
   lowerCamelCase__:      Dict			  =crop_size if crop_size is not None else {"height": 30, "width": 30}
   lowerCamelCase__:      Any			  =parent
   lowerCamelCase__:      Any			  =batch_size
   lowerCamelCase__:      Optional[Any]			  =num_channels
   lowerCamelCase__:      Tuple			  =min_resolution
   lowerCamelCase__:      Union[str, Any]			  =max_resolution
   lowerCamelCase__:      Union[str, Any]			  =do_resize_and_center_crop
   lowerCamelCase__:      Optional[int]			  =size
   lowerCamelCase__:      str			  =crop_pct
   lowerCamelCase__:      Any			  =crop_size
   lowerCamelCase__:      List[str]			  =do_normalize
   lowerCamelCase__:      List[str]			  =image_mean
   lowerCamelCase__:      Tuple			  =image_std







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return {
       "size": self.size,
       "do_resize_and_center_crop": self.do_resize_and_center_crop,
       "crop_pct": self.crop_pct,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }



@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			PoolFormerImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =PoolFormerImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->int:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize_and_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "crop_pct"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size       , {"shortest_edge": 30})
   self.assertEqual(image_processor.crop_size       , {"height": 30, "width": 30})

   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class.from_dict(self.image_processor_dict       , size=42       , crop_size=84)
   self.assertEqual(image_processor.size       , {"shortest_edge": 42})
   self.assertEqual(image_processor.crop_size       , {"height": 84, "width": 84})

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[Any]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      Union[str, Any]			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , Image.Image)

   # Test not batched input
   lowerCamelCase__:      Dict			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values

   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      int			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:      Tuple			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , np.ndarray)

   # Test not batched input
   lowerCamelCase__:      Union[str, Any]			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      List[str]			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )







 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:      Any			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , torch.Tensor)

   # Test not batched input
   lowerCamelCase__:      Any			  =image_processing(image_inputs[0]       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      str			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

