






'''simple docstring'''




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 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					List[str]   =       argparse.ArgumentParser()
			parser.add_argument(
			    "-m"     ,"--pretrained_model_name_or_path"     ,type=__magic_name__     ,default=__magic_name__     ,required=__magic_name__     ,help="Path to pretrained model or model identifier from huggingface.co/models."     ,)
			parser.add_argument(
			    "-c"     ,"--caption"     ,type=__magic_name__     ,default="robotic cat with wings"     ,help="Text used to generate images."     ,)
			parser.add_argument(
			    "-n"     ,"--images_num"     ,type=__magic_name__     ,default=4     ,help="How much images to generate."     ,)
			parser.add_argument(
			    "-s"     ,"--seed"     ,type=__magic_name__     ,default=42     ,help="Seed for random process."     ,)
			parser.add_argument(
			    "-ci"     ,"--cuda_id"     ,type=__magic_name__     ,default=0     ,help="cuda_id."     ,)
			snake_case_				:					Union[str, Any]   =       parser.parse_args()
			return args

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			if not len(__magic_name__						) == rows * cols:
						raise ValueError("The specified number of rows and columns are not correct."						)

			snake_case_				:					Optional[Any]   =       imgs[0].size
			snake_case_				:					int   =       Image.new("RGB"     ,size=(cols * w, rows * h)						)
			snake_case_				:					Any   =       grid.size

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__="robotic cat with wings"     ,__magic_name__=7.5     ,__magic_name__=50     ,__magic_name__=1     ,__magic_name__=42     ,)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					List[str]   =       torch.Generator(pipeline.device						).manual_seed(__magic_name__						)
			snake_case_				:					Any   =       pipeline(
			    __magic_name__     ,guidance_scale=__magic_name__     ,num_inference_steps=__magic_name__     ,generator=__magic_name__     ,num_images_per_prompt=__magic_name__     ,).images
			snake_case_				:					Optional[Any]   =       int(math.sqrt(__magic_name__						)						)
			snake_case_				:					Dict   =       image_grid(__magic_name__     ,rows=_rows     ,cols=num_images_per_prompt // _rows						)
			return grid, images


__lowerCamelCase   :					Dict   							=					parse_args()
# Load models and create wrapper for stable diffusion
__lowerCamelCase   :					Any   							=					CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__lowerCamelCase   :					str   							=					CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__lowerCamelCase   :					List[str]   							=					AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__lowerCamelCase   :					Union[str, Any]   							=					UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

__lowerCamelCase   :					str   							=					StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCamelCase   :					Any   							=					lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
		__lowerCamelCase   :					List[str]   							=					load(args.pretrained_model_name_or_path, model=unet)
		unet.eval()
		setattr(pipeline, '''unet''', unet)
else:
		__lowerCamelCase   :					str   							=					unet.to(torch.device('''cuda''', args.cuda_id))
__lowerCamelCase   :					int   							=					pipeline.to(unet.device)
__lowerCamelCase   :					List[Any]   							=					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()))))
__lowerCamelCase   :					Tuple   							=					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)))












'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	__UpperCAmelCase       ( __magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if subparsers is not None:
						snake_case_				:					List[str]   =       subparsers.add_parser("test"						)
			else:
						snake_case_				:					List[Any]   =       argparse.ArgumentParser("Accelerate test command"						)

			parser.add_argument(
			    "--config_file"     ,default=__magic_name__     ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
			        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with 'huggingface'."
			    )     ,)

			if subparsers is not None:
						parser.set_defaults(func=__magic_name__						)
			return parser

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ["test_utils", "scripts", "test_script.py"]						)

			if args.config_file is None:
						snake_case_				:					str   =       script_name
			else:
						snake_case_				:					Any   =       F'''--config_file={args.config_file} {script_name}'''

			snake_case_				:					Union[str, Any]   =       ["accelerate-launch"] + test_args.split()
			snake_case_				:					Optional[int]   =       execute_subprocess_async(__magic_name__     ,env=os.environ.copy()						)
			if result.returncode == 0:
						print("Test is a success! You are ready for your distributed training!"						)

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       test_command_parser()
			snake_case_				:					Dict   =       parser.parse_args()
			test_command(__magic_name__						)


if __name__ == "__main__":
		main()




