



'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__,	unittest.TestCase      ):
			A__								=  IFSuperResolutionPipeline
			A__								=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
			A__								=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			A__								=  PipelineTesterMixin.required_optional_params - {'latents'}



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							return self._get_superresolution_dummy_components()



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[str]=0			)      ->      List[str]:
							"""simple docstring"""





							if str(__UpperCamelCase			).startswith('''mps'''			):
											snake_case__      :   Optional[Any]      =    torch.manual_seed(__UpperCamelCase			)
							else:
											snake_case__      :   Any      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

							snake_case__      :   Dict      =    floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)

							snake_case__      :   List[Any]      =    {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': 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 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != '''cuda'''    ,			reason='''float16 requires CUDA'''			)
			def 				lowerCAmelCase (     self		:						str			)      ->      List[Any]:
							"""simple docstring"""





							super().test_save_load_floataa(expected_max_diff=1e-1			)



			def 				lowerCAmelCase (     self		:						Tuple			)      ->      Optional[Any]:
							"""simple docstring"""





							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



			def 				lowerCAmelCase (     self		:						str			)      ->      Optional[int]:
							"""simple docstring"""





							self._test_save_load_local()







			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,			)









'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class _A	(     unittest.TestCase     ):





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

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  inspect.getfile(accelerate.test_utils					)
					__UpperCAmelCase :   int    					=  os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_sync.py"""]					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main			,							num_processes=1					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main					)





		@require_single_gpu
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					test_sync.main()





		@require_multi_gpu
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					print(f'Found {torch.cuda.device_count()} devices.'					)
					__UpperCAmelCase :   List[str]    					=  ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1					):
								execute_subprocess_async(__UpperCAmelCase			,							env=os.environ.copy()					)




