

'''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 a():





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





def a(lowercase__			, lowercase__			, lowercase__	):





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

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

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





def a(lowercase__			, lowercase__="robotic cat with wings"			, lowercase__=7.5			, lowercase__=50			, lowercase__=1			, lowercase__=42			, ):





	'''simple docstring'''
	snake_case_      		= torch.Generator(pipeline.device	).manual_seed(lowercase__	)
	snake_case_      		= pipeline(
	    lowercase__			, guidance_scale=lowercase__			, num_inference_steps=lowercase__			, generator=lowercase__			, num_images_per_prompt=lowercase__			, ).images
	snake_case_      		= int(math.sqrt(lowercase__	)	)
	snake_case_      		= image_grid(lowercase__			, rows=_rows			, cols=num_images_per_prompt // _rows	)
	return grid, images


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

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



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
