


'''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_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      Dict					=  IFSuperResolutionPipeline
			__a					:      Dict					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
			__a					:      Tuple					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__a					:      Tuple					=  PipelineTesterMixin.required_optional_params - {'''latents'''}




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								return self._get_superresolution_dummy_components()




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						Union[str, Any]:


								'''simple docstring'''






								if str(lowercase       ).startswith('''mps'''       ):
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.manual_seed(lowercase       )
								else:
													__SCREAMING_SNAKE_CASE					:							int			   = torch.Generator(device=lowercase       ).manual_seed(lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = floats_tensor((1, 3, 3_2, 3_2)					,      rng=random.Random(lowercase       )       ).to(lowercase       )

								__SCREAMING_SNAKE_CASE					:							List[str]			   = {
								    '''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 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != '''cuda'''					,      reason='''float16 requires CUDA'''       )
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






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




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_save_load_local()




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






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






'''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_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      Dict					=  IFSuperResolutionPipeline
			__a					:      Dict					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
			__a					:      Tuple					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__a					:      Tuple					=  PipelineTesterMixin.required_optional_params - {'''latents'''}




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								return self._get_superresolution_dummy_components()




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						Union[str, Any]:


								'''simple docstring'''






								if str(lowercase       ).startswith('''mps'''       ):
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.manual_seed(lowercase       )
								else:
													__SCREAMING_SNAKE_CASE					:							int			   = torch.Generator(device=lowercase       ).manual_seed(lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = floats_tensor((1, 3, 3_2, 3_2)					,      rng=random.Random(lowercase       )       ).to(lowercase       )

								__SCREAMING_SNAKE_CASE					:							List[str]			   = {
								    '''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 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != '''cuda'''					,      reason='''float16 requires CUDA'''       )
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






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




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_save_load_local()




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






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


