


'''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				(      )  ->     Optional[int]:



					'''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     Tuple:



					'''simple docstring'''






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

					UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			imgs[0].size
					UpperCamelCase__       :Any							 =			Image.new('''RGB'''			, size=(cols * w, rows * h)       )
					UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			grid.size

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


def       a				(      __a			, __a="robotic cat with wings"			, __a=7.5			, __a=50			, __a=1			, __a=42			, )  ->     Dict:



					'''simple docstring'''






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


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

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


def       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			list(range(0			, __a       )       )

					UpperCamelCase__       :Optional[int]							 =			[item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					UpperCamelCase__       :Optional[int]							 =			[]
					for i in device_map_blocks:
										if device_map_blocks.count(__a       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(__a       )
    # Missing blocks
					UpperCamelCase__       :List[str]							 =			[i for i in blocks if i not in device_map_blocks]
					UpperCamelCase__       :Optional[Any]							 =			[i for i in device_map_blocks if i not in blocks]

					if len(__a       ) != 0:
										raise ValueError(
										    '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
										    ''' These attention blocks were specified more than once: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
										    '''blocks to a device on the device_map: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(__a       )       )







def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			list(range(__a       )       )
					UpperCamelCase__       :Any							 =			int(ceil(n_layers / len(__a       )       )       )
					UpperCamelCase__       :List[Any]							 =			[layers[i : i + n_blocks] for i in range(0			, __a			, __a       )]

					return dict(zip(__a			, __a       )       )