



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   UpperCAmelCase_    (       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):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase     :							str    =    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=__a,			)
														_lowerCAmelCase     :							Optional[int]    =    PNDMScheduler(skip_prk_steps=__a)
														torch.manual_seed(0)
														_lowerCAmelCase     :							List[Any]    =    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=128,			)
														torch.manual_seed(0)
														_lowerCAmelCase     :							Union[str, Any]    =    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=1000,			hidden_act="gelu",			projection_dim=512,			)
														_lowerCAmelCase     :							List[Any]    =    CLIPTextModel(__a)
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

														_lowerCAmelCase     :							str    =    {
														    "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):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    floats_tensor((1, 3, 32, 32),			rng=random.Random(__a)).to(__a)
														_lowerCAmelCase     :							Optional[int]    =    image.cpu().permute(0,			2,			3,			1)[0]
														_lowerCAmelCase     :							Dict    =    Image.fromarray(np.uinta(__a)).convert("RGB").resize((64, 64))
														_lowerCAmelCase     :							List[str]    =    Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
														if str(__a).startswith("mps"):
																					_lowerCAmelCase     :							Any    =    torch.manual_seed(__a)
														else:
																					_lowerCAmelCase     :							int    =    torch.Generator(device=__a).manual_seed(__a)
														_lowerCAmelCase     :							Optional[Any]    =    {
														    "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):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_dummy_components()
														_lowerCAmelCase     :							List[str]    =    StableDiffusionInpaintPipeline(**__a)
														_lowerCAmelCase     :							Tuple    =    sd_pipe.to(__a)
														sd_pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase     :							List[Any]    =    self.get_dummy_inputs(__a)
														_lowerCAmelCase     :							Dict    =    sd_pipe(**__a).images
														_lowerCAmelCase     :							Any    =    image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														_lowerCAmelCase     :							Tuple    =    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):

														'''simple docstring'''

														super().test_inference_batch_single_identical(expected_max_diff=3E-3)



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







							def    snake_case__      (   self):

														'''simple docstring'''

														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png")
														_lowerCAmelCase     :							Dict    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
														_lowerCAmelCase     :							Tuple    =    load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
														    "/yellow_cat_sitting_on_a_park_bench.npy")

														_lowerCAmelCase     :							List[Any]    =    "stabilityai/stable-diffusion-2-inpainting"
														_lowerCAmelCase     :							int    =    StableDiffusionInpaintPipeline.from_pretrained(__a,			safety_checker=__a)
														pipe.to(__a)
														pipe.set_progress_bar_config(disable=__a)
														pipe.enable_attention_slicing()

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

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

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image).max() < 9E-3







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png")
														_lowerCAmelCase     :							Tuple    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
														_lowerCAmelCase     :							str    =    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     :							Optional[Any]    =    "stabilityai/stable-diffusion-2-inpainting"
														_lowerCAmelCase     :							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()

														_lowerCAmelCase     :							Optional[Any]    =    "Face of a yellow cat, high resolution, sitting on a park bench"

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

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image).max() < 5E-1







							def    snake_case__      (   self):

														'''simple docstring'''

														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

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

														_lowerCAmelCase     :							Optional[Any]    =    "stabilityai/stable-diffusion-2-inpainting"
														_lowerCAmelCase     :							Tuple    =    PNDMScheduler.from_pretrained(__a,			subfolder="scheduler")
														_lowerCAmelCase     :							int    =    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()

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

														_lowerCAmelCase     :							int    =    torch.manual_seed(0)
														_lowerCAmelCase     :							Union[str, Any]    =    pipe(
														    prompt=__a,			image=__a,			mask_image=__a,			generator=__a,			num_inference_steps=2,			output_type="np",			)

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





import argparse
from collections import defaultdict

import yaml


_snake_case		=  "docs/source/en/_toctree.yml"






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    defaultdict(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    []
							_lowerCAmelCase     :							List[str]    =    []
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({"local": doc["local"], "title": doc["title"]}  )
														else:
																					new_doc_list.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Optional[Any]    =    new_doc_list
							_lowerCAmelCase     :							List[Any]    =    [key for key, value in counts.items() if value > 1]

							_lowerCAmelCase     :							str    =    []
							for duplicate_key in duplicates:
														_lowerCAmelCase     :							List[str]    =    list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}  )
														if len(_lowerCamelCase  ) > 1:
																					raise ValueError(
																					    F"{duplicate_key} is present several times in the documentation table of content at "
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."  )
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]  )
							_lowerCAmelCase     :							Optional[Any]    =    sorted(_lowerCamelCase				, key=lambda _lowerCamelCase  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(_lowerCamelCase  ) > 1:
														raise ValueError("{doc_list} has two 'overview' docs which is not allowed."  )

							overview_doc.extend(_lowerCamelCase  )

							# Sort
							return overview_doc






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							int    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[Any]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							List[str]    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							Union[str, Any]    =    0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase     :							Optional[Any]    =    api_doc[scheduler_idx]["sections"]
							_lowerCAmelCase     :							Optional[Any]    =    clean_doc_toc(_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase     :							List[Any]    =    True
														if overwrite:
																					_lowerCAmelCase     :							Dict    =    new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Tuple    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."  )






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							Tuple    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[int]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							int    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							List[str]    =    0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase     :							Dict    =    False
							_lowerCAmelCase     :							Optional[int]    =    api_doc[pipeline_idx]["sections"]
							_lowerCAmelCase     :							Tuple    =    []

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase     :							List[Any]    =    pipeline_doc["section"]
																					_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )
																					if overwrite:
																												_lowerCAmelCase     :							Optional[Any]    =    new_sub_pipeline_doc
														new_pipeline_docs.append(_lowerCamelCase  )

							# sort overall pipeline doc
							_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase     :							Dict    =    True
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						_snake_case		=  parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)
