






"""simple docstring"""
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowerCamelCase_       			=   argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowerCamelCase_			,	lowerCamelCase_       			=   parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'pip install -r transformers/examples/{example_dir}/requirements.txt'])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)











"""simple docstring"""
import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler

def        snake_case   (     A__			,A__			,A__=[]		):
			UpperCAmelCase_  :				int      =      size[0] - overlap_pixels * 2
			UpperCAmelCase_  :				Dict      =      size[1] - overlap_pixels * 2
			for letter in ["l", "r"]:
						if letter in remove_borders:
									size_x += overlap_pixels
			for letter in ["t", "b"]:
						if letter in remove_borders:
									size_y += overlap_pixels
			UpperCAmelCase_  :				Union[str, Any]      =      np.ones((size_y, size_x)			,dtype=np.uinta		) * 2_55
			UpperCAmelCase_  :				Optional[int]      =      np.pad(A__			,mode="linear_ramp"			,pad_width=A__			,end_values=0		)

			if "l" in remove_borders:
						UpperCAmelCase_  :				List[str]      =      mask[:, overlap_pixels : mask.shape[1]]
			if "r" in remove_borders:
						UpperCAmelCase_  :				Any      =      mask[:, 0 : mask.shape[1] - overlap_pixels]
			if "t" in remove_borders:
						UpperCAmelCase_  :				Optional[int]      =      mask[overlap_pixels : mask.shape[0], :]
			if "b" in remove_borders:
						UpperCAmelCase_  :				Any      =      mask[0 : mask.shape[0] - overlap_pixels, :]
			return mask

def        snake_case   (     A__			,A__			,A__		):
			return max(A__			,min(A__			,A__		)		)

def        snake_case   (     A__			,A__			,A__		):
			return (
			    clamp(rect[0]			,min[0]			,max[0]		),
			    clamp(rect[1]			,min[1]			,max[1]		),
			    clamp(rect[2]			,min[0]			,max[0]		),
			    clamp(rect[3]			,min[1]			,max[1]		),
			)

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      list(A__		)
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			UpperCAmelCase_  :				Optional[Any]      =      clamp_rect(A__			,[0, 0]			,[image_size[0], image_size[1]]		)
			return rect

def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      Image.new("RGB"			,(tile.size[0] + original_slice, tile.size[1])		)
			result.paste(
			    original_image.resize((tile.size[0], tile.size[1])			,Image.BICUBIC		).crop(
			        (slice_x, 0, slice_x + original_slice, tile.size[1])		)			,(0, 0)			,)
			result.paste(A__			,(original_slice, 0)		)
			return result

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Optional[int]      =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
			UpperCAmelCase_  :				Optional[int]      =      tile.crop(A__		)
			return tile

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      n % d
			return n - divisor







class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				AutoencoderKL			,			lowerCAmelCase_      :				CLIPTextModel			,			lowerCAmelCase_      :				CLIPTokenizer			,			lowerCAmelCase_      :				UNetaDConditionModel			,			lowerCAmelCase_      :				DDPMScheduler			,			lowerCAmelCase_      :				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,			lowerCAmelCase_      :				int = 350			,			)		->    Optional[Any]:
				super().__init__(
				    vae=lowerCAmelCase_			,			text_encoder=lowerCAmelCase_			,			tokenizer=lowerCAmelCase_			,			unet=lowerCAmelCase_			,			low_res_scheduler=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			max_noise_level=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				List[str]		)		->    Dict:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Optional[Any]      =      (
				    min(image.size[0] - (tile_size + original_image_slice)			,			x * tile_size		),
				    min(image.size[1] - (tile_size + original_image_slice)			,			y * tile_size		),
				    min(image.size[0]			,			(x + 1) * tile_size		),
				    min(image.size[1]			,			(y + 1) * tile_size		),
				)
				UpperCAmelCase_  :				Dict      =      add_overlap_rect(lowerCAmelCase_			,			lowerCAmelCase_			,			image.size		)
				UpperCAmelCase_  :				Tuple      =      image.crop(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
				UpperCAmelCase_  :				int      =      translated_slice_x - (original_image_slice / 2)
				UpperCAmelCase_  :				Any      =      max(0			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      squeeze_tile(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      to_input.size
				UpperCAmelCase_  :				Optional[int]      =      to_input.resize((tile_size, tile_size)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				Optional[Any]      =      super(lowerCAmelCase_			,			self		).__call__(image=lowerCAmelCase_			,			**lowerCAmelCase_		).images[0]
				UpperCAmelCase_  :				Union[str, Any]      =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      unsqueeze_tile(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      []
				if x == 0:
							remove_borders.append("l"		)
				elif crop_rect[2] == image.size[0]:
							remove_borders.append("r"		)
				if y == 0:
							remove_borders.append("t"		)
				elif crop_rect[3] == image.size[1]:
							remove_borders.append("b"		)
				UpperCAmelCase_  :				str      =      Image.fromarray(
				    make_transparency_mask(
				        (upscaled_tile.size[0], upscaled_tile.size[1])			,			tile_border * 4			,			remove_borders=lowerCAmelCase_		)			,			mode="L"			,			)
				final_image.paste(
				    lowerCAmelCase_			,			(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)			,			lowerCAmelCase_		)





	@torch.no_grad()
	def __call__(   self      :				int			,			lowerCAmelCase_      :				Union[str, List[str]]			,			lowerCAmelCase_      :				Union[PIL.Image.Image, List[PIL.Image.Image]]			,			lowerCAmelCase_      :				int = 75			,			lowerCAmelCase_      :				float = 9.0			,			lowerCAmelCase_      :				int = 50			,			lowerCAmelCase_      :				Optional[Union[str, List[str]]] = None			,			lowerCAmelCase_      :				Optional[int] = 1			,			lowerCAmelCase_      :				float = 0.0			,			lowerCAmelCase_      :				Optional[torch.Generator] = None			,			lowerCAmelCase_      :				Optional[torch.FloatTensor] = None			,			lowerCAmelCase_      :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				int = 128			,			lowerCAmelCase_      :				int = 32			,			lowerCAmelCase_      :				int = 32			,			)		->    int:
				UpperCAmelCase_  :				List[Any]      =      Image.new("RGB"			,			(image.size[0] * 4, image.size[1] * 4)		)
				UpperCAmelCase_  :				Any      =      math.ceil(image.size[0] / tile_size		)
				UpperCAmelCase_  :				str      =      math.ceil(image.size[1] / tile_size		)
				UpperCAmelCase_  :				Any      =      tcx * tcy
				UpperCAmelCase_  :				List[Any]      =      0
				for y in range(lowerCAmelCase_		):
							for x in range(lowerCAmelCase_		):
										self._process_tile(
										    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			prompt=lowerCAmelCase_			,			num_inference_steps=lowerCAmelCase_			,			guidance_scale=lowerCAmelCase_			,			noise_level=lowerCAmelCase_			,			negative_prompt=lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_			,			eta=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			latents=lowerCAmelCase_			,			)
										current_count += 1
										if callback is not None:
													callback({"progress": current_count / total_tile_count, "image": final_image}		)
				return final_image





def        snake_case   (     ):
			# Run a demo
			UpperCAmelCase_  :				List[str]      =      "stabilityai/stable-diffusion-x4-upscaler"
			UpperCAmelCase_  :				Optional[Any]      =      StableDiffusionTiledUpscalePipeline.from_pretrained(A__			,revision="fp16"			,torch_dtype=torch.floataa		)
			UpperCAmelCase_  :				Optional[int]      =      pipe.to("cuda"		)
			UpperCAmelCase_  :				Tuple      =      Image.open("../../docs/source/imgs/diffusers_library.jpg"		)

			def callback(A__		):
						print(F"""progress: {obj['progress']:.4f}"""		)
						obj["image"].save("diffusers_library_progress.jpg"		)

			UpperCAmelCase_  :				Union[str, Any]      =      pipe(image=A__			,prompt="Black font, white background, vector"			,noise_level=40			,callback=A__		)
			final_image.save("diffusers_library.jpg"		)


if __name__ == "__main__":
			main()



