





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     SCREAMING_SNAKE_CASE__ (						a__				,					a__				,					a__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	StableDiffusionInpaintPipeline
 __SCREAMING_SNAKE_CASE							       =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE							       =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE							       =	frozenset(
     []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE							       =	frozenset([]      )



 def 				UpperCamelCase			(							self ):
        torch.manual_seed(0 )
        A__											=						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=_UpperCAmelCase,)
        A__											=						PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
        torch.manual_seed(0 )
        A__											=						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 )
        A__											=						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,)
        A__											=						CLIPTextModel(_UpperCAmelCase )
        A__											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        A__											=						{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=0 ):
        A__											=						floats_tensor((1, 3, 32, 32),rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
        A__											=						image.cpu().permute(0,2,3,1 )[0]
        A__											=						Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
        A__											=						Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
        if str(_UpperCAmelCase ).startswith('''mps''' ):
               A__											=						torch.manual_seed(_UpperCAmelCase )
        else:
               A__											=						torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
        A__											=						{
            '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 ):
        A__											=						'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__											=						self.get_dummy_components()
        A__											=						StableDiffusionInpaintPipeline(**_UpperCAmelCase )
        A__											=						sd_pipe.to(_UpperCAmelCase )
        sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

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

        assert image.shape == (1, 64, 64, 3)
        A__											=						np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )

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




 def 				UpperCamelCase			(							self ):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




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



 def 				UpperCamelCase			(							self ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        A__											=						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
        A__											=						load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench.npy''' )

        A__											=						'stabilityai/stable-diffusion-2-inpainting'
        A__											=						StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase,safety_checker=_UpperCAmelCase )
        pipe.to(_UpperCAmelCase )
        pipe.set_progress_bar_config(disable=_UpperCAmelCase )
        pipe.enable_attention_slicing()

        A__											=						'Face of a yellow cat, high resolution, sitting on a park bench'

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        A__											=						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
        A__											=						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__											=						'stabilityai/stable-diffusion-2-inpainting'
        A__											=						StableDiffusionInpaintPipeline.from_pretrained(
            _UpperCAmelCase,torch_dtype=torch.floataa,safety_checker=_UpperCAmelCase,)
        pipe.to(_UpperCAmelCase )
        pipe.set_progress_bar_config(disable=_UpperCAmelCase )
        pipe.enable_attention_slicing()

        A__											=						'Face of a yellow cat, high resolution, sitting on a park bench'

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

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




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

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

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

        A__											=						'Face of a yellow cat, high resolution, sitting on a park bench'

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

        A__											=						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 argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()