



def 			a		( _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      [7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
		for gap in gaps:
				for i in range(_UpperCAmelCase					,  len(_UpperCAmelCase		)		):
						__UpperCAmelCase       :	List[Any]						=      collection[i]
						__UpperCAmelCase       :	Dict						=      i
						while j >= gap and collection[j - gap] > insert_value:
								__UpperCAmelCase       :	Union[str, Any]						=      collection[j - gap]
								j -= gap
						if j != i:
								__UpperCAmelCase       :	str						=      insert_value
		return collection


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				__A       =input("Enter numbers separated by a comma:\n").strip()
				__A       =[int(item) for item in user_input.split(",")]
				print(shell_sort(unsorted))









import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )