
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'''
				lowercase_       =					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=42						,   help='''Seed for random process.'''						,   )
				parser.add_argument(
				    '''-ci'''						,   '''--cuda_id'''						,   type=snake_case__						,   default=0						,   help='''cuda_id.'''						,   )
				lowercase_       =					parser.parse_args()
				return args




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Tuple						,   snake_case__:					Union[str, Any]					):


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

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

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




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Union[str, Any]="robotic cat with wings"						,   snake_case__:					Union[str, Any]=7.5						,   snake_case__:					List[str]=50						,   snake_case__:					List[Any]=1						,   snake_case__:					Optional[int]=42						,   ):


				'''simple docstring'''
				lowercase_       =					torch.Generator(pipeline.device					).manual_seed(snake_case__					)
				lowercase_       =					pipeline(
				    snake_case__						,   guidance_scale=snake_case__						,   num_inference_steps=snake_case__						,   generator=snake_case__						,   num_images_per_prompt=snake_case__						,   ).images
				lowercase_       =					int(math.sqrt(snake_case__					)					)
				lowercase_       =					image_grid(snake_case__						,   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)))








def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		if collection == []:
				return []

		# get some information about the collection
		__lowercase=			len(lowercase__	)
		__lowercase=			max(lowercase__	)
		__lowercase=			min(lowercase__	)

		# create the counting array
		__lowercase=			coll_max + 1 - coll_min
		__lowercase=			[0] * counting_arr_length

		# count how much a number appears in the collection
		for number in collection:
				counting_arr[number - coll_min] += 1

		# sum each position with it's predecessors. now, counting_arr[i] tells
		# us how many elements <= i has in the collection
		for i in range(1   , lowercase__	):
				__lowercase=			counting_arr[i] + counting_arr[i - 1]

		# create the output collection
		__lowercase=			[0] * coll_len

		# place the elements in the output, respecting the original order (stable
		# sort) from end to begin, updating counting_arr
		for i in reversed(range(0   , lowercase__	)	):
				__lowercase=			collection[i]
				counting_arr[collection[i] - coll_min] -= 1

		return ordered







def        _lowerCamelCase(	lowercase__	)						->	List[str]:

		'''simple docstring'''







		return "".join([chr(lowercase__	) for i in counting_sort([ord(lowercase__	) for c in string]	)]	)


if __name__ == "__main__":
	# Test string sort
	assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

	lowerCAmelCase           =		input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase           =		[int(item) for item in user_input.split(''',''')]
	print(counting_sort(unsorted))






