


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 	_a   (   __snake_case		,		__snake_case		,		__snake_case		,		unittest.TestCase			):
    a_     :					Optional[Any]   							=     StableDiffusionInpaintPipeline
    a_     :					str   							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    a_     :					Tuple   							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a_     :					List[Any]   							=     frozenset(
        []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a_     :					str   							=     frozenset([]			)
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          torch.manual_seed(0					)
          lowerCamelCase__							     =		UNetaDConditionModel(
              block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=9				,       out_channels=4				,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,       cross_attention_dim=32				,       attention_head_dim=(2, 4)				,       use_linear_projection=SCREAMING_SNAKE_CASE__				,       )
          lowerCamelCase__							     =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__					)
          torch.manual_seed(0					)
          lowerCamelCase__							     =		AutoencoderKL(
              block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,       latent_channels=4				,       sample_size=1_28				,       )
          torch.manual_seed(0					)
          lowerCamelCase__							     =		CLIPTextConfig(
              bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=10_00				,       hidden_act='gelu'				,       projection_dim=5_12				,       )
          lowerCamelCase__							     =		CLIPTextModel(SCREAMING_SNAKE_CASE__					)
          lowerCamelCase__							     =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

          lowerCamelCase__							     =		{
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
    def   _UpperCamelCase							(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       List[str]=0					):
          # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
          lowerCamelCase__							     =		floats_tensor((1, 3, 32, 32)				,       rng=random.Random(SCREAMING_SNAKE_CASE__					)					).to(SCREAMING_SNAKE_CASE__					)
          lowerCamelCase__							     =		image.cpu().permute(0				,       2				,       3				,       1					)[0]
          lowerCamelCase__							     =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__					)					).convert('RGB'					).resize((64, 64)					)
          lowerCamelCase__							     =		Image.fromarray(np.uinta(image + 4					)					).convert('RGB'					).resize((64, 64)					)
          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__							     =		{
              """prompt""": """A painting of a squirrel eating a burger""",
              """image""": init_image,
              """mask_image""": mask_image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs
    def   _UpperCamelCase							(			self				:       Tuple					):
          lowerCamelCase__							     =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__							     =		self.get_dummy_components()
          lowerCamelCase__							     =		StableDiffusionInpaintPipeline(**SCREAMING_SNAKE_CASE__					)
          lowerCamelCase__							     =		sd_pipe.to(SCREAMING_SNAKE_CASE__					)
          sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

          lowerCamelCase__							     =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					)
          lowerCamelCase__							     =		sd_pipe(**SCREAMING_SNAKE_CASE__					).images
          lowerCamelCase__							     =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          lowerCamelCase__							     =		np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2


    def   _UpperCamelCase							(			self				:       int					):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class 	_a   (   unittest.TestCase			):
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def   _UpperCamelCase							(			self				:       int					):
          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png'					)
          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'					)
          lowerCamelCase__							     =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
              '/yellow_cat_sitting_on_a_park_bench.npy'					)

          lowerCamelCase__							     =		"""stabilityai/stable-diffusion-2-inpainting"""
          lowerCamelCase__							     =		StableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE__				,       safety_checker=SCREAMING_SNAKE_CASE__					)
          pipe.to(SCREAMING_SNAKE_CASE__					)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)
          pipe.enable_attention_slicing()

          lowerCamelCase__							     =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

          lowerCamelCase__							     =		torch.manual_seed(0					)
          lowerCamelCase__							     =		pipe(
              prompt=SCREAMING_SNAKE_CASE__				,       image=SCREAMING_SNAKE_CASE__				,       mask_image=SCREAMING_SNAKE_CASE__				,       generator=SCREAMING_SNAKE_CASE__				,       output_type='np'				,       )
          lowerCamelCase__							     =		output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image					).max() < 9e-3
    def   _UpperCamelCase							(			self				:       Any					):
          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png'					)
          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'					)
          lowerCamelCase__							     =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
              '/yellow_cat_sitting_on_a_park_bench_fp16.npy'					)

          lowerCamelCase__							     =		"""stabilityai/stable-diffusion-2-inpainting"""
          lowerCamelCase__							     =		StableDiffusionInpaintPipeline.from_pretrained(
              SCREAMING_SNAKE_CASE__				,       torch_dtype=torch.floataa				,       safety_checker=SCREAMING_SNAKE_CASE__				,       )
          pipe.to(SCREAMING_SNAKE_CASE__					)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)
          pipe.enable_attention_slicing()

          lowerCamelCase__							     =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

          lowerCamelCase__							     =		torch.manual_seed(0					)
          lowerCamelCase__							     =		pipe(
              prompt=SCREAMING_SNAKE_CASE__				,       image=SCREAMING_SNAKE_CASE__				,       mask_image=SCREAMING_SNAKE_CASE__				,       generator=SCREAMING_SNAKE_CASE__				,       output_type='np'				,       )
          lowerCamelCase__							     =		output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image					).max() < 5e-1


    def   _UpperCamelCase							(			self				:       Optional[int]					):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png'					)
          lowerCamelCase__							     =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'					)

          lowerCamelCase__							     =		"""stabilityai/stable-diffusion-2-inpainting"""
          lowerCamelCase__							     =		PNDMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__				,       subfolder='scheduler'					)
          lowerCamelCase__							     =		StableDiffusionInpaintPipeline.from_pretrained(
              SCREAMING_SNAKE_CASE__				,       safety_checker=SCREAMING_SNAKE_CASE__				,       scheduler=SCREAMING_SNAKE_CASE__				,       torch_dtype=torch.floataa				,       )
          pipe.to(SCREAMING_SNAKE_CASE__					)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)
          pipe.enable_attention_slicing(1					)
          pipe.enable_sequential_cpu_offload()

          lowerCamelCase__							     =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

          lowerCamelCase__							     =		torch.manual_seed(0					)
          lowerCamelCase__							     =		pipe(
              prompt=SCREAMING_SNAKE_CASE__				,       image=SCREAMING_SNAKE_CASE__				,       mask_image=SCREAMING_SNAKE_CASE__				,       generator=SCREAMING_SNAKE_CASE__				,       num_inference_steps=2				,       output_type='np'				,       )

          lowerCamelCase__							     =		torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.65 * 10**9











"""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
