

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







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

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

       for i, img in enumerate(__UpperCAmelCase				):
              grid.paste(__UpperCAmelCase,       box=(i % cols * w, i // cols * h)				)
       return grid
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__="robotic cat with wings",       UpperCAmelCase__=7.5,       UpperCAmelCase__=50,       UpperCAmelCase__=1,       UpperCAmelCase__=42,       )			->			Optional[Any]:
       A_						=      torch.Generator(pipeline.device				).manual_seed(__UpperCAmelCase				)
       A_						=      pipeline(
           __UpperCAmelCase,       guidance_scale=__UpperCAmelCase,       num_inference_steps=__UpperCAmelCase,       generator=__UpperCAmelCase,       num_images_per_prompt=__UpperCAmelCase,       ).images
       A_						=      int(math.sqrt(__UpperCAmelCase				)				)
       A_						=      image_grid(__UpperCAmelCase,       rows=_rows,       cols=num_images_per_prompt // _rows				)
       return grid, images


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

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







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      0.01
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     A_						=      time.time()
                     locka.acquire(UpperCAmelCase__				)
                     assert time.time() - _start > timeout







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       A_						=      """a""" * 10_00 + """.lock"""
       A_						=      FileLock(str(tmpdir / filename				)				)
       assert locka._lock_file.endswith(""".lock"""				)
       assert not locka._lock_file.endswith(UpperCAmelCase__				)
       assert len(os.path.basename(locka._lock_file				)				) <= 2_55
       A_						=      FileLock(tmpdir / filename				)
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     locka.acquire(0				)
