


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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 	lowerCamelCase_       (				lowerCAmelCase__     ,     lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				IFInpaintingPipeline
					__UpperCAmelCase            =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					__UpperCAmelCase            =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__UpperCAmelCase            =				PipelineTesterMixin.required_optional_params - {"latents"}






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return self._get_dummy_components()






					def A      (				self ,       snake_case_ ,       snake_case_=0						)   ->			List[Any]:







								'''simple docstring'''

								if str(snake_case_						).startswith('''mps'''						):
											__lowercase 					=   torch.manual_seed(snake_case_						)
								else:
											__lowercase 					=   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)

								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(snake_case_						)						).to(snake_case_						)
								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(snake_case_						)						).to(snake_case_						)

								__lowercase 					=   {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': 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 A      (				self						)   ->			Any:







								'''simple docstring'''

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								self._test_save_load_optional_components()






					@unittest.skipIf(torch_device != '''cuda''' ,       reason='''float16 requires CUDA'''						)
					def A      (				self						)   ->			Dict:







								'''simple docstring'''

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






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







								'''simple docstring'''

								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								self._test_save_load_local()






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







								'''simple docstring'''

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






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


