






# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__      :			List[str]							=		TypeVar("""T""")
class 						lowerCamelCase_				(      Generic[T] ):







				def __init__(						self       ,					__lowerCAmelCase = True     ):
								"""simple docstring"""
								__magic_name__		:dict[T, list[T]]        =      {}  # dictionary of lists
								__magic_name__		:Tuple        =      directed







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if not self.directed:  # For undirected graphs
												# if both source vertex and destination vertex are both present in the
												# adjacency list, add destination vertex to source vertex list of adjacent
												# vertices and add source vertex to destination vertex list of adjacent
												# vertices.
												if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																self.adj_list[source_vertex].append(__lowerCAmelCase     )
																self.adj_list[destination_vertex].append(__lowerCAmelCase     )
												# if only source vertex is present in adjacency list, add destination vertex
												# to source vertex list of adjacent vertices, then create a new vertex with
												# destination vertex as key and assign a list containing the source vertex
												# as it's first adjacent vertex.
												elif source_vertex in self.adj_list:
																self.adj_list[source_vertex].append(__lowerCAmelCase     )
																__magic_name__		:Union[str, Any]        =      [source_vertex]
												# if only destination vertex is present in adjacency list, add source vertex
												# to destination vertex list of adjacent vertices, then create a new vertex
												# with source vertex as key and assign a list containing the source vertex
												# as it's first adjacent vertex.
												elif destination_vertex in self.adj_list:
																self.adj_list[destination_vertex].append(__lowerCAmelCase     )
																__magic_name__		:Union[str, Any]        =      [destination_vertex]
												# if both source vertex and destination vertex are not present in adjacency
												# list, create a new vertex with source vertex as key and assign a list
												# containing the destination vertex as it's first adjacent vertex also
												# create a new vertex with destination vertex as key and assign a list
												# containing the source vertex as it's first adjacent vertex.
												else:
																__magic_name__		:Dict        =      [destination_vertex]
																__magic_name__		:Optional[int]        =      [source_vertex]
								else:  # For directed graphs
												# if both source vertex and destination vertex are present in adjacency
												# list, add destination vertex to source vertex list of adjacent vertices.
												if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																self.adj_list[source_vertex].append(__lowerCAmelCase     )
												# if only source vertex is present in adjacency list, add destination
												# vertex to source vertex list of adjacent vertices and create a new vertex
												# with destination vertex as key, which has no adjacent vertex
												elif source_vertex in self.adj_list:
																self.adj_list[source_vertex].append(__lowerCAmelCase     )
																__magic_name__		:Any        =      []
												# if only destination vertex is present in adjacency list, create a new
												# vertex with source vertex as key and assign a list containing destination
												# vertex as first adjacent vertex
												elif destination_vertex in self.adj_list:
																__magic_name__		:Any        =      [destination_vertex]
												# if both source vertex and destination vertex are not present in adjacency
												# list, create a new vertex with source vertex as key and a list containing
												# destination vertex as it's first adjacent vertex. Then create a new vertex
												# with destination vertex as key, which has no adjacent vertex
												else:
																__magic_name__		:Union[str, Any]        =      [destination_vertex]
																__magic_name__		:Union[str, Any]        =      []

								return self







				def __repr__(						self     ):
								"""simple docstring"""
								return pformat(self.adj_list     )














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      __lowercase					( ):







				"""simple docstring"""






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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

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

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

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






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


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

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






