





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    IFInpaintingSuperResolutionPipeline
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}   )
					A__				 =    PipelineTesterMixin.required_optional_params - {"latents"}





					def 					__magic_name__ ( self	):
										return self._get_superresolution_dummy_components()





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=0	):
										if str(_lowerCAmelCase	).startswith("mps"	):
															lowerCamelCase__       =       torch.manual_seed(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)

										lowerCamelCase__       =       floats_tensor((1, 3, 16, 16)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)

										lowerCamelCase__       =       {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "original_image": original_image,
										    "mask_image": mask_image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs





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





					def 					__magic_name__ ( self	):
										self._test_save_load_optional_components()





					@unittest.skipIf(torch_device != "cuda"	,  reason="float16 requires CUDA"	)
					def 					__magic_name__ ( 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	)





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





					def 					__magic_name__ ( self	):
										self._test_save_load_local()






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








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A_												=     Lock()





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a) ->Optional[int]:
					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0,     10):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(a)
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															lowerCamelCase__       =       min(a,     a)
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(a)
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															lowerCamelCase__       =       max(a,     a)
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(a)





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe())
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					lowerCamelCase__       =       Pipe()
					lowerCamelCase__       =       Pipe()
					process_array_.append(
					    Process(
					        target=a,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),     ))
					lowerCamelCase__       =       temp_rs
					lowerCamelCase__       =       temp_rr

					for i in range(1,     len(a) - 1):
										lowerCamelCase__       =       Pipe()
										lowerCamelCase__       =       Pipe()
										process_array_.append(
										    Process(
										        target=a,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),     ))
										lowerCamelCase__       =       temp_rs
										lowerCamelCase__       =       temp_rr

					process_array_.append(
					    Process(
					        target=a,     args=(
					            len(a) - 1,
					            arr[len(a) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(a) - 1],
					        ),     ))

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0,     len(a)):
										lowerCamelCase__       =       result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def    __UpperCamelCase      (    ) ->List[Any]:
					lowerCamelCase__       =       list(range(10,     0,     -1))
					print("Initial List")
					print(*a)
					lowerCamelCase__       =       odd_even_transposition(a)
					print("Sorted List\n")
					print(*a)


if __name__ == "__main__":
						main()

