





'''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 			SCREAMING_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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						:			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=_UpperCAmelCase						,     )
          __A						:			int								=	PNDMScheduler(skip_prk_steps=_UpperCAmelCase)
          torch.manual_seed(0)
          __A						:			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)
          __A						:			int								=	CLIPTextConfig(
              bos_token_id=0						,     eos_token_id=2						,     hidden_size=32						,     intermediate_size=37						,     layer_norm_eps=1e-0_5						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1000						,     hidden_act='gelu'						,     projection_dim=512						,     )
          __A						:			Union[str, Any]								=	CLIPTextModel(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=0):

          '''simple docstring'''






          __A						:			List[str]								=	floats_tensor((1, 3, 32, 32)						,     rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						:			List[str]								=	image.cpu().permute(0						,     2						,     3						,     1)[0]
          __A						:			Any								=	Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((64, 64))
          __A						:			str								=	Image.fromarray(np.uinta(image + 4)).convert('RGB').resize((64, 64))
          if str(_UpperCAmelCase).startswith('mps'):
               __A						:			Any								=	torch.manual_seed(_UpperCAmelCase)
          else:
               __A						:			List[str]								=	torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						:			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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A						:			Dict								=	self.get_dummy_components()
          __A						:			Dict								=	StableDiffusionInpaintPipeline(**_UpperCAmelCase)
          __A						:			Dict								=	sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

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

          assert image.shape == (1, 64, 64, 3)
          __A						:			int								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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




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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Any								=	'stabilityai/stable-diffusion-2-inpainting'
          __A						:			List[Any]								=	StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase						,     safety_checker=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()

          __A						:			Dict								=	'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						:			str								=	torch.manual_seed(0)
          __A						:			Dict								=	pipe(
              prompt=_UpperCAmelCase						,     image=_UpperCAmelCase						,     mask_image=_UpperCAmelCase						,     generator=_UpperCAmelCase						,     output_type='np'						,     )
          __A						:			Any								=	output.images[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png')
          __A						:			Tuple								=	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_fp16.npy')

          __A						:			Optional[Any]								=	'stabilityai/stable-diffusion-2-inpainting'
          __A						:			Any								=	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						:			Optional[Any]								=	'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						:			Tuple								=	torch.manual_seed(0)
          __A						:			Optional[int]								=	pipe(
              prompt=_UpperCAmelCase						,     image=_UpperCAmelCase						,     mask_image=_UpperCAmelCase						,     generator=_UpperCAmelCase						,     output_type='np'						,     )
          __A						:			Dict								=	output.images[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Union[str, 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						:			Tuple								=	'stabilityai/stable-diffusion-2-inpainting'
          __A						:			Any								=	PNDMScheduler.from_pretrained(_UpperCAmelCase						,     subfolder='scheduler')
          __A						:			Union[str, Any]								=	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						:			int								=	'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						:			Optional[Any]								=	torch.manual_seed(0)
          __A						:			str								=	pipe(
              prompt=_UpperCAmelCase						,     image=_UpperCAmelCase						,     mask_image=_UpperCAmelCase						,     generator=_UpperCAmelCase						,     num_inference_steps=2						,     output_type='np'						,     )

          __A						:			Optional[int]								=	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 arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)