





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 _a (  )	->    Tuple:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  List[str]       )	->    Optional[Any]:

							'''simple docstring'''






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

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

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




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Tuple="robotic cat with wings"			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=7.5			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]=50			,				SCREAMING_SNAKE_CASE__      :  List[Any]=1			,				SCREAMING_SNAKE_CASE__      :  int=42			,				)	->    Union[str, Any]:

							'''simple docstring'''






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


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

_lowerCamelCase :    List[str]    							= StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowerCamelCase :    int    							= lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
		_lowerCamelCase :    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 :    Union[str, 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 :    Dict    							= 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 pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    "split_dict"			,				[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42			,				dataset_name="my_dataset"       )}       ),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42       )}       ),
        SplitDict({"train": SplitInfo()}       ),
    ]			,				)
def _a (  SCREAMING_SNAKE_CASE__      :  SplitDict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						split_dict._to_yaml_list()
							assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE__       )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														SCREAMING_SNAKE_CASE__       :      str     =						None
														# the split name of split_dict takes over the name of the split info object
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						split_name
							assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"			,				[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE__       ), SplitInfo(dataset_name="my_dataset"       )]       )
def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						asdict(SplitDict({"train": split_info}       )       )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name






