


"""simple docstring"""


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 	_a   (   __snake_case		,		__snake_case		,		unittest.TestCase			):
    a_     :					int   							=     IFInpaintingPipeline
    a_     :					List[Any]   							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    a_     :					Optional[Any]   							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a_     :					Optional[Any]   							=     PipelineTesterMixin.required_optional_params - {"""latents"""}
    def   _UpperCamelCase							(			self				:       Optional[int]					):
          return self._get_dummy_components()
    def   _UpperCamelCase							(			self				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       Optional[int]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=0					):
          if str(SCREAMING_SNAKE_CASE__					).startswith('mps'					):
                lowerCamelCase__							     =		torch.manual_seed(SCREAMING_SNAKE_CASE__					)
          else:
                lowerCamelCase__							     =		torch.Generator(device=SCREAMING_SNAKE_CASE__					).manual_seed(SCREAMING_SNAKE_CASE__					)

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

          lowerCamelCase__							     =		{
              """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   _UpperCamelCase							(			self				:       List[str]					):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)
    def   _UpperCamelCase							(			self				:       Any					):
          self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'					)
    def   _UpperCamelCase							(			self				:       Dict					):
          # 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   _UpperCamelCase							(			self				:       Tuple					):
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)
    def   _UpperCamelCase							(			self				:       str					):
          self._test_save_load_local()


    def   _UpperCamelCase							(			self				:       List[Any]					):
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2				,       )











"""simple docstring"""







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


_lowerCAmelCase            =	logging.get_logger(__name__)


@dataclass
class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(		self  :Union[str, Any]			, **__magic_name__  :int				)      ->Any:
										for deprecated_arg in self.deprecated_args:
														if deprecated_arg in kwargs:
																		lowercase    :      int       = deprecated_arg[3:]
																		setattr(self			, __magic_name__			, not kwargs.pop(__magic_name__				)				)
																		logger.warning(
																		    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																		    f""" {positive_arg}={kwargs[positive_arg]}"""				)

										lowercase    :      List[str]       = kwargs.pop("""torchscript"""			, self.torchscript				)
										lowercase    :      Any       = kwargs.pop("""torch_xla_tpu_print_metrics"""			, self.torch_xla_tpu_print_metrics				)
										lowercase    :      Union[str, Any]       = kwargs.pop("""fp16_opt_level"""			, self.fpaa_opt_level				)
										super().__init__(**__magic_name__				)

						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Trace the models using torchscript"""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default="""O1"""						,						metadata={
						        """help""": (
						            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
						            """See details at https://nvidia.github.io/apex/amp.html"""
						        )
						    }						,						)






						@cached_property
						def      __snake_case      (		self  :Optional[int]				)      ->Tuple["torch.device", int]:
										requires_backends(self			, ["""torch"""]				)
										logger.info("""PyTorch: setting up devices"""				)
										if not self.cuda:
														lowercase    :      Optional[Any]       = torch.device("""cpu"""				)
														lowercase    :      int       = 0
										elif is_torch_tpu_available():
														lowercase    :      Any       = xm.xla_device()
														lowercase    :      List[str]       = 0
										else:
														lowercase    :      int       = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
														lowercase    :      Any       = torch.cuda.device_count()
										return device, n_gpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return is_torch_tpu_available() and self.tpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										requires_backends(self			, ["""torch"""]				)
										# TODO(PVP): currently only single GPU is supported
										return torch.cuda.current_device()






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->"torch.device":
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[0]






						@property
						def      __snake_case      (		self  :Optional[Any]				)      ->str:
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[1]






						@property
						def      __snake_case      (		self  :int				)      ->List[str]:
										return self.n_gpu > 0
