
'''simple docstring'''







from __future__ import annotations





def 					UpperCAmelCase_		(__a      :    list[int] ):






   """simple docstring"""

   if len(__a ) == 0:
      return array

   _a,  _a				:	Any			=   min(__a ), max(__a )

   # Compute the variables
   _a				:	int			=   _max - _min + 1
   _a,  _a				:	List[str]			=   [0] * holes_range, [0] * holes_range

   # Make the sorting.
   for i in array:
      _a				:	List[str]			=   i - _min
      _a				:	Union[str, Any]			=   i
      holes_repeat[index] += 1

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

    # Returns the sorted array.
   return array


if __name__ == "__main__":
  import doctest

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





'''simple docstring'''







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   VQModel(
         block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=3							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   CLIPTextConfig(
         bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
     return CLIPTextModel(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

     _a				:	str			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance



