


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						(_lowerCAmelCase	,							_lowerCAmelCase	,							_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Dict	    =    StableDiffusionInpaintPipeline
							__UpperCAmelCase       :				List[str]	    =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							__UpperCAmelCase       :				Union[str, Any]	    =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__UpperCAmelCase       :				int	    =    frozenset(
							    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							__UpperCAmelCase       :				List[Any]	    =    frozenset([] )

							def        __snake_case   (				self			:    List[Any]							)      ->       Tuple:
														torch.manual_seed(0							)
														__snake_case						:     Optional[Any]									=  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=lowerCamelCase       ,      )
														__snake_case						:     Any									=  PNDMScheduler(skip_prk_steps=lowerCamelCase							)
														torch.manual_seed(0							)
														__snake_case						:     Optional[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							)
														__snake_case						:     int									=  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       ,      )
														__snake_case						:     Optional[Any]									=  CLIPTextModel(lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    str=0							)      ->       Any:
														# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
														__snake_case						:     Any									=  floats_tensor((1, 3, 32, 32)       ,      rng=random.Random(lowerCamelCase							)							).to(lowerCamelCase							)
														__snake_case						:     str									=  image.cpu().permute(0       ,      2       ,      3       ,      1							)[0]
														__snake_case						:     Dict									=  Image.fromarray(np.uinta(lowerCamelCase							)							).convert("RGB"							).resize((64, 64)							)
														__snake_case						:     str									=  Image.fromarray(np.uinta(image + 4							)							).convert("RGB"							).resize((64, 64)							)
														if str(lowerCamelCase							).startswith("mps"							):
																					__snake_case						:     List[Any]									=  torch.manual_seed(lowerCamelCase							)
														else:
																					__snake_case						:     Dict									=  torch.Generator(device=lowerCamelCase							).manual_seed(lowerCamelCase							)
														__snake_case						:     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			:    Union[str, Any]							)      ->       List[str]:
														__snake_case						:     List[Any]									=  "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case						:     List[Any]									=  self.get_dummy_components()
														__snake_case						:     List[Any]									=  StableDiffusionInpaintPipeline(**lowerCamelCase							)
														__snake_case						:     List[Any]									=  sd_pipe.to(lowerCamelCase							)
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase							)

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

														assert image.shape == (1, 64, 64, 3)
														__snake_case						:     Dict									=  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        __snake_case   (				self			:    Dict							)      ->       Any:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3							)

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




							"""simple docstring"""


							def        __snake_case   (				self			:    Union[str, Any]							)      ->       List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

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

														__snake_case						:     int									=  "stabilityai/stable-diffusion-2-inpainting"
														__snake_case						:     Tuple									=  StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase       ,      safety_checker=lowerCamelCase							)
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)
														pipe.enable_attention_slicing()

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

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

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

							def        __snake_case   (				self			:    Dict							)      ->       Any:
														__snake_case						:     Union[str, Any]									=  load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png"							)
														__snake_case						:     str									=  load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)
														__snake_case						:     Union[str, 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"							)

														__snake_case						:     Any									=  "stabilityai/stable-diffusion-2-inpainting"
														__snake_case						:     Dict									=  StableDiffusionInpaintPipeline.from_pretrained(
														    lowerCamelCase       ,      torch_dtype=torch.floataa       ,      safety_checker=lowerCamelCase       ,      )
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)
														pipe.enable_attention_slicing()

														__snake_case						:     str									=  "Face of a yellow cat, high resolution, sitting on a park bench"

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

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






							def        __snake_case   (				self			:    str							)      ->       Optional[Any]:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

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

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

														__snake_case						:     str									=  "Face of a yellow cat, high resolution, sitting on a park bench"

														__snake_case						:     List[str]									=  torch.manual_seed(0							)
														__snake_case						:     str									=  pipe(
														    prompt=lowerCamelCase       ,      image=lowerCamelCase       ,      mask_image=lowerCamelCase       ,      generator=lowerCamelCase       ,      num_inference_steps=2       ,      output_type="np"       ,      )

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


import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	huggingface_hub.__version__

       lowerCAmelCase_:							List[str]       =	"not installed"
       lowerCAmelCase_:							Dict       =	"NA"
       if is_torch_available():
          import torch

          lowerCAmelCase_:							List[str]       =	torch.__version__
          lowerCAmelCase_:							int       =	torch.cuda.is_available()

       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_transformers_available():
          import transformers

          lowerCAmelCase_:							Tuple       =	transformers.__version__

       lowerCAmelCase_:							Optional[int]       =	"not installed"
       if is_accelerate_available():
          import accelerate

          lowerCAmelCase_:							List[Any]       =	accelerate.__version__

       lowerCAmelCase_:							Dict       =	"not installed"
       if is_xformers_available():
          import xformers

          lowerCAmelCase_:							Dict       =	xformers.__version__

       lowerCAmelCase_:							Union[str, Any]       =	{
           "`diffusers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Huggingface_hub version": hub_version,
           "Transformers version": transformers_version,
           "Accelerate version": accelerate_version,
           "xFormers version": xformers_version,
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
       print(self.format_dict(lowerCamelCase__ ) )

       return info




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"