

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__      ( )     ->     List[Any]:
       A_		   = 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=42,    help="""Seed for random process.""",    )
       parser.add_argument(
           """-ci""",    """--cuda_id""",    type=__lowerCamelCase,    default=0,    help="""cuda_id.""",    )
       A_		   = parser.parse_args()
       return args
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[str]:
       if not len(__lowerCamelCase			) == rows * cols:
              raise ValueError("""The specified number of rows and columns are not correct."""			)

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

       for i, img in enumerate(__lowerCamelCase			):
              grid.paste(__lowerCamelCase,    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,    )     ->     str:
       A_		   = torch.Generator(pipeline.device			).manual_seed(__lowerCamelCase			)
       A_		   = pipeline(
           __lowerCamelCase,    guidance_scale=__lowerCamelCase,    num_inference_steps=__lowerCamelCase,    generator=__lowerCamelCase,    num_images_per_prompt=__lowerCamelCase,    ).images
       A_		   = int(math.sqrt(__lowerCamelCase			)			)
       A_		   = image_grid(__lowerCamelCase,    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 json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """repo_id""": str(UpperCAmelCase__			),
           """repo_sha""": str(repo.head.object.hexsha			),
           """repo_branch""": str(repo.active_branch			),
       }

       with open(os.path.join(UpperCAmelCase__,    """git_log.json"""			),    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__,    indent=4			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs"""			)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              A_		   = int(os.environ["""WORLD_SIZE"""]			)
              A_		   = int(os.environ["""N_GPU_NODE"""]			)
              A_		   = int(os.environ["""RANK"""]			)

              # number of nodes / node ID
              A_		   = params.world_size // params.n_gpu_per_node
              A_		   = params.global_rank // params.n_gpu_per_node
              A_		   = True

              assert params.n_nodes == int(os.environ["""N_NODES"""]			)
              assert params.node_id == int(os.environ["""NODE_RANK"""]			)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes			)
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id			)
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank			)
       logger.info(PREFIX + """World size     : %i""" % params.world_size			)
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node			)
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master			)			)
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node			)			)
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu			)			)
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()			)

       # set GPU device
       torch.cuda.set_device(params.local_rank			)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed"""			)
              torch.distributed.init_process_group(
                  init_method="""env://""",    backend="""nccl""",    )







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
