






"""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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase=[]       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    size[0] - overlap_pixels * 2
				_SCREAMING_SNAKE_CASE   :		str       		=    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
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    np.ones((size_y, size_x),						dtype=np.uinta       ) * 255
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    np.pad(lowerCamelCase,						mode='linear_ramp',						pad_width=lowerCamelCase,						end_values=0       )

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





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				return max(lowerCamelCase,						min(lowerCamelCase,						lowerCamelCase       )       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    list(lowerCamelCase       )
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				_SCREAMING_SNAKE_CASE   :		int       		=    clamp_rect(lowerCamelCase,						[0, 0],						[image_size[0], image_size[1]]       )
				return rect





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    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(lowerCamelCase,						(original_slice, 0)       )
				return result





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    tile.crop(lowerCamelCase       )
				return tile





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    n % d
				return n - divisor


class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 3_5_0   ,				)		->							Optional[Any]:
										super().__init__(
										    vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				unet=lowerCAmelCase_   ,				low_res_scheduler=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				max_noise_level=lowerCAmelCase_   ,				)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Union[str, Any]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		str       		=    (
										    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							),
										)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    add_overlap_rect(lowerCAmelCase_   ,				lowerCAmelCase_   ,				image.size							)
										_SCREAMING_SNAKE_CASE   :		str       		=    image.crop(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    translated_slice_x - (original_image_slice / 2)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    max(0   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    squeeze_tile(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    to_input.size
										_SCREAMING_SNAKE_CASE   :		int       		=    to_input.resize((tile_size, tile_size)   ,				Image.BICUBIC							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    super(lowerCAmelCase_   ,				self							).__call__(image=lowerCAmelCase_   ,				**lowerCAmelCase_							).images[0]
										_SCREAMING_SNAKE_CASE   :		Any       		=    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)   ,				Image.BICUBIC							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    unsqueeze_tile(lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)   ,				Image.BICUBIC							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    []
										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'							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 7_5   ,				lowerCAmelCase_ = 9.0   ,				lowerCAmelCase_ = 5_0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 0.0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Image.new('RGB'   ,				(image.size[0] * 4, image.size[1] * 4)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    math.ceil(image.size[0] / tile_size							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    math.ceil(image.size[1] / tile_size							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tcx * tcy
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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 				lowercase__		(					):
				# Run a demo
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'stabilityai/stable-diffusion-x4-upscaler'
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCamelCase,						revision='fp16',						torch_dtype=torch.floataa       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipe.to('cuda'       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Image.open('../../docs/source/imgs/diffusers_library.jpg'       )

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

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    pipe(image=lowerCamelCase,						prompt='Black font, white background, vector',						noise_level=40,						callback=lowerCamelCase       )
				final_image.save('diffusers_library.jpg'       )


if __name__ == "__main__":
			main()









"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def 				lowercase__		(					lowerCamelCase       ):
				return EnvironmentCommand()






class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    parser.add_parser('env'							)
										download_parser.set_defaults(func=lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    huggingface_hub.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'not installed'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'NA'
										if is_torch_available():
														import torch

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.__version__
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.is_available()

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'not installed'
										if is_transformers_available():
														import transformers

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    transformers.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'not installed'
										if is_accelerate_available():
														import accelerate

														_SCREAMING_SNAKE_CASE   :		str       		=    accelerate.__version__

										_SCREAMING_SNAKE_CASE   :		str       		=    'not installed'
										if is_xformers_available():
														import xformers

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xformers.__version__

										_SCREAMING_SNAKE_CASE   :		Any       		=    {
										    '`diffusers` version': version,
										    'Platform': platform.platform(),
										    'Python version': platform.python_version(),
										    'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
										    'Huggingface_hub version': hub_version,
										    'Transformers version': transformers_version,
										    'Accelerate version': accelerate_version,
										    'xFormers version': xformers_version,
										    'Using GPU in script?': '<fill in>',
										    'Using distributed or parallel set-up in script?': '<fill in>',
										}

										print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'							)
										print(self.format_dict(lowerCAmelCase_							)							)

										return info







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"

