

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	__lowerCamelCase    :    int 	=			argparse.ArgumentParser()
	parser.add_argument(
	    '-m'     ,			'--pretrained_model_name_or_path'     ,			type=lowerCamelCase__     ,			default=lowerCamelCase__     ,			required=lowerCamelCase__     ,			help='Path to pretrained model or model identifier from huggingface.co/models.'     ,			)
	parser.add_argument(
	    '-c'     ,			'--caption'     ,			type=lowerCamelCase__     ,			default='robotic cat with wings'     ,			help='Text used to generate images.'     ,			)
	parser.add_argument(
	    '-n'     ,			'--images_num'     ,			type=lowerCamelCase__     ,			default=4     ,			help='How much images to generate.'     ,			)
	parser.add_argument(
	    '-s'     ,			'--seed'     ,			type=lowerCamelCase__     ,			default=4_2     ,			help='Seed for random process.'     ,			)
	parser.add_argument(
	    '-ci'     ,			'--cuda_id'     ,			type=lowerCamelCase__     ,			default=0     ,			help='cuda_id.'     ,			)
	__lowerCamelCase    :    Optional[Any] 	=			parser.parse_args()
	return args






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	if not len(lowerCamelCase__      ) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'      )

	__lowerCamelCase    :    Union[str, Any] 	=			imgs[0].size
	__lowerCamelCase    :    int 	=			Image.new('RGB'     ,			size=(cols * w, rows * h)      )
	__lowerCamelCase    :    Any 	=			grid.size

	for i, img in enumerate(lowerCamelCase__      ):
		grid.paste(lowerCamelCase__     ,			box=(i % cols * w, i // cols * h)      )
	return grid






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__="robotic cat with wings"     ,			lowerCamelCase__=7.5     ,			lowerCamelCase__=5_0     ,			lowerCamelCase__=1     ,			lowerCamelCase__=4_2     ,			)  ->					str:
	__lowerCamelCase    :    int 	=			torch.Generator(pipeline.device      ).manual_seed(lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			pipeline(
	    lowerCamelCase__     ,			guidance_scale=lowerCamelCase__     ,			num_inference_steps=lowerCamelCase__     ,			generator=lowerCamelCase__     ,			num_images_per_prompt=lowerCamelCase__     ,			).images
	__lowerCamelCase    :    Optional[int] 	=			int(math.sqrt(lowerCamelCase__      )      )
	__lowerCamelCase    :    Union[str, Any] 	=			image_grid(lowerCamelCase__     ,			rows=_rows     ,			cols=num_images_per_prompt // _rows      )
	return grid, images


a		=parse_args()
# Load models and create wrapper for stable diffusion
a		=CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
a		=CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
a		=AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
a		=UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

a		=StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a		=lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
				a		=load(args.pretrained_model_name_or_path, model=unet)
				unet.eval()
				setattr(pipeline, """unet""", unet)
else:
				a		=unet.to(torch.device("""cuda""", args.cuda_id))
a		=pipeline.to(unet.device)
a		,	a		=generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
a		=os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
				image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
					import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy')

								# using the PNDM scheduler by default
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




