



"""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 			snake_case_						(						a_     ,a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					StableDiffusionInpaintPipeline
							__lowerCAmelCase    					=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							__lowerCAmelCase    					=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__lowerCAmelCase    					=					frozenset(
							    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							__lowerCAmelCase    					=					frozenset([]				)






							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=9			,     out_channels=4			,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,     cross_attention_dim=3_2			,     attention_head_dim=(2, 4)			,     use_linear_projection=a_			,     )
									a_     :      Optional[Any] 						=     PNDMScheduler(skip_prk_steps=a_      )
									torch.manual_seed(0      )
									a_     :      Optional[Any] 						=     AutoencoderKL(
									    block_out_channels=[3_2, 6_4]			,     in_channels=3			,     out_channels=3			,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,     latent_channels=4			,     sample_size=1_2_8			,     )
									torch.manual_seed(0      )
									a_     :      List[str] 						=     CLIPTextConfig(
									    bos_token_id=0			,     eos_token_id=2			,     hidden_size=3_2			,     intermediate_size=3_7			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1_0_0_0			,     hidden_act="gelu"			,     projection_dim=5_1_2			,     )
									a_     :      Tuple 						=     CLIPTextModel(a_      )
									a_     :      List[str] 						=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

									a_     :      Tuple 						=     {
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									a_     :      str 						=     floats_tensor((1, 3, 3_2, 3_2)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Optional[int] 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      Optional[Any] 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
									a_     :      List[Any] 						=     Image.fromarray(np.uinta(image + 4      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
									if str(a_      ).startswith("mps"      ):
											a_     :      Optional[Any] 						=     torch.manual_seed(a_      )
									else:
											a_     :      Tuple 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      str 						=     {
									    "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 snake_case_  (   self      ):
									a_     :      List[str] 						=     "cpu"  # ensure determinism for the device-dependent torch.Generator
									a_     :      Tuple 						=     self.get_dummy_components()
									a_     :      Dict 						=     StableDiffusionInpaintPipeline(**a_      )
									a_     :      int 						=     sd_pipe.to(a_      )
									sd_pipe.set_progress_bar_config(disable=a_      )

									a_     :      str 						=     self.get_dummy_inputs(a_      )
									a_     :      Optional[int] 						=     sd_pipe(**a_      ).images
									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									a_     :      str 						=     np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]      )

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






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




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png"      )
									a_     :      List[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"      )
									a_     :      Optional[Any] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy"      )

									a_     :      Dict 						=     "stabilityai/stable-diffusion-2-inpainting"
									a_     :      str 						=     StableDiffusionInpaintPipeline.from_pretrained(a_			,     safety_checker=a_      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )
									pipe.enable_attention_slicing()

									a_     :      Union[str, Any] 						=     "Face of a yellow cat, high resolution, sitting on a park bench"

									a_     :      List[Any] 						=     torch.manual_seed(0      )
									a_     :      Any 						=     pipe(
									    prompt=a_			,     image=a_			,     mask_image=a_			,     generator=a_			,     output_type="np"			,     )
									a_     :      List[Any] 						=     output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image      ).max() < 9e-3






							def snake_case_  (   self      ):
									a_     :      int 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png"      )
									a_     :      Any 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"      )
									a_     :      Any 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"      )

									a_     :      Optional[int] 						=     "stabilityai/stable-diffusion-2-inpainting"
									a_     :      Any 						=     StableDiffusionInpaintPipeline.from_pretrained(
									    a_			,     torch_dtype=torch.floataa			,     safety_checker=a_			,     )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )
									pipe.enable_attention_slicing()

									a_     :      Any 						=     "Face of a yellow cat, high resolution, sitting on a park bench"

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      str 						=     pipe(
									    prompt=a_			,     image=a_			,     mask_image=a_			,     generator=a_			,     output_type="np"			,     )
									a_     :      int 						=     output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image      ).max() < 5e-1






							def snake_case_  (   self      ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png"      )
									a_     :      List[str] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"      )

									a_     :      str 						=     "stabilityai/stable-diffusion-2-inpainting"
									a_     :      List[Any] 						=     PNDMScheduler.from_pretrained(a_			,     subfolder="scheduler"      )
									a_     :      List[str] 						=     StableDiffusionInpaintPipeline.from_pretrained(
									    a_			,     safety_checker=a_			,     scheduler=a_			,     torch_dtype=torch.floataa			,     )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )
									pipe.enable_attention_slicing(1      )
									pipe.enable_sequential_cpu_offload()

									a_     :      Tuple 						=     "Face of a yellow cat, high resolution, sitting on a park bench"

									a_     :      List[Any] 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pipe(
									    prompt=a_			,     image=a_			,     mask_image=a_			,     generator=a_			,     num_inference_steps=2			,     output_type="np"			,     )

									a_     :      Any 						=     torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 1_0**9




"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)