


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    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=0.9 ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					):




              """simple docstring"""




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






       def  _lowercase   (     self						):




              """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    UpperCAmelCase_	(							UpperCamelCase_,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					PoolFormerImageProcessor if is_vision_available() else None






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		PoolFormerImageProcessingTester(self						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.image_processor_tester.prepare_image_processor_dict()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.image_processing_class(**self.image_processor_dict						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''do_resize_and_center_crop'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''size'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''crop_pct'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''do_normalize'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''image_mean'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''image_std'''						)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		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}						)

              UpperCamelCase    :	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  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.image_processing_class(**self.image_processor_dict						)
              # create random PIL images
              UpperCamelCase    :	Dict   	=		prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__SCREAMING_SNAKE_CASE						)
              for image in image_inputs:
                     self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					Image.Image						)

              # Test not batched input
              UpperCamelCase    :	Optional[int]   	=		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
              UpperCamelCase    :	List[Any]   	=		image_processing(__SCREAMING_SNAKE_CASE ,					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  _lowercase   (     self						):




              """simple docstring"""




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

              # Test not batched input
              UpperCamelCase    :	List[str]   	=		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
              UpperCamelCase    :	int   	=		image_processing(__SCREAMING_SNAKE_CASE ,					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  _lowercase   (     self						):




              """simple docstring"""




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

              # Test not batched input
              UpperCamelCase    :	int   	=		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
              UpperCamelCase    :	Any   	=		image_processing(__SCREAMING_SNAKE_CASE ,					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'''],
                  ) ,					)





from __future__ import annotations


def      a	(							SCREAMING_SNAKE_CASE_      :		list[int]       ):






       """simple docstring"""





       if len(SCREAMING_SNAKE_CASE_       ) == 0:
              return array

       UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		min(SCREAMING_SNAKE_CASE_       ), max(SCREAMING_SNAKE_CASE_       )

       # Compute the variables
       UpperCamelCase    :	Union[str, Any]   	=		_max - _min + 1
       UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		[0] * holes_range, [0] * holes_range

       # Make the sorting.
       for i in array:
              UpperCamelCase    :	Optional[int]   	=		i - _min
              UpperCamelCase    :	Any   	=		i
              holes_repeat[index] += 1

       # Makes the array back by replacing the numbers.
       UpperCamelCase    :	str   	=		0
       for i in range(SCREAMING_SNAKE_CASE_       ):
              while holes_repeat[i] > 0:
                     UpperCamelCase    :	List[Any]   	=		holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase : Any 			=				input("Enter numbers separated by comma:\n")
  __UpperCAmelCase : int 			=				[int(x) for x in user_input.split(",")]
  print(pigeon_sort(unsorted))

