




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

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







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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				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			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    list(lowercase__		)
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				_lowerCamelCase     :			str    =    clamp_rect(lowercase__							,    [0, 0]							,    [image_size[0], image_size[1]]		)
				return rect







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    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(lowercase__							,    (original_slice, 0)		)
				return result







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				_lowerCamelCase     :			Optional[int]    =    tile.crop(lowercase__		)
				return tile







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    n % d
				return n - divisor






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = 350  ,      ):
							super().__init__(
							    vae=lowercase  ,      text_encoder=lowercase  ,      tokenizer=lowercase  ,      unet=lowercase  ,      low_res_scheduler=lowercase  ,      scheduler=lowercase  ,      max_noise_level=lowercase  ,      )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      **lowercase  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			Optional[int]    =    (
							    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  ),
							)
							_lowerCamelCase     :			str    =    add_overlap_rect(lowercase  ,      lowercase  ,      image.size  )
							_lowerCamelCase     :			Dict    =    image.crop(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
							_lowerCamelCase     :			List[str]    =    translated_slice_x - (original_image_slice / 2)
							_lowerCamelCase     :			List[Any]    =    max(0  ,      lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    squeeze_tile(lowercase  ,      lowercase  ,      lowercase  ,      lowercase  )
							_lowerCamelCase     :			int    =    to_input.size
							_lowerCamelCase     :			Union[str, Any]    =    to_input.resize((tile_size, tile_size)  ,      Image.BICUBIC  )
							_lowerCamelCase     :			Union[str, Any]    =    super(lowercase  ,      self  ).__call__(image=lowercase  ,      **lowercase  ).images[0]
							_lowerCamelCase     :			Tuple    =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)  ,      Image.BICUBIC  )
							_lowerCamelCase     :			List[Any]    =    unsqueeze_tile(lowercase  ,      lowercase  )
							_lowerCamelCase     :			Dict    =    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)  ,      Image.BICUBIC  )
							_lowerCamelCase     :			Dict    =    []
							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'  )
							_lowerCamelCase     :			str    =    Image.fromarray(
							    make_transparency_mask(
							        (upscaled_tile.size[0], upscaled_tile.size[1])  ,      tile_border * 4  ,      remove_borders=lowercase  )  ,      mode='L'  ,      )
							final_image.paste(
							    lowercase  ,      (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)  ,      lowercase  )






			@torch.no_grad()
			def __call__(  self  ,      lowercase  ,      lowercase  ,      lowercase = 75  ,      lowercase = 9.0  ,      lowercase = 50  ,      lowercase = None  ,      lowercase = 1  ,      lowercase = 0.0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 1  ,      lowercase = 128  ,      lowercase = 32  ,      lowercase = 32  ,      ):
							_lowerCamelCase     :			Any    =    Image.new('RGB'  ,      (image.size[0] * 4, image.size[1] * 4)  )
							_lowerCamelCase     :			Optional[int]    =    math.ceil(image.size[0] / tile_size  )
							_lowerCamelCase     :			Optional[Any]    =    math.ceil(image.size[1] / tile_size  )
							_lowerCamelCase     :			Dict    =    tcx * tcy
							_lowerCamelCase     :			List[str]    =    0
							for y in range(lowercase  ):
											for x in range(lowercase  ):
															self._process_tile(
															    lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      prompt=lowercase  ,      num_inference_steps=lowercase  ,      guidance_scale=lowercase  ,      noise_level=lowercase  ,      negative_prompt=lowercase  ,      num_images_per_prompt=lowercase  ,      eta=lowercase  ,      generator=lowercase  ,      latents=lowercase  ,      )
															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
				_lowerCamelCase     :			int    =    'stabilityai/stable-diffusion-x4-upscaler'
				_lowerCamelCase     :			Optional[Any]    =    StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase__							,    revision='fp16'							,    torch_dtype=torch.floataa		)
				_lowerCamelCase     :			Optional[int]    =    pipe.to('cuda'		)
				_lowerCamelCase     :			List[Any]    =    Image.open('../../docs/source/imgs/diffusers_library.jpg'		)

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

				_lowerCamelCase     :			str    =    pipe(image=lowercase__							,    prompt='Black font, white background, vector'							,    noise_level=40							,    callback=lowercase__		)
				final_image.save('diffusers_library.jpg'		)


if __name__ == "__main__":
			main()





"""simple docstring"""






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase__     						=						get_logger(__name__)






class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			"""dummy_data"""
			lowerCamelCase__      =			"""datasets"""
			lowerCamelCase__      =			False
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    0
							_lowerCamelCase     :			Dict    =    dataset_name
							_lowerCamelCase     :			Union[str, Any]    =    cache_dir
							_lowerCamelCase     :			Dict    =    use_local_dummy_data
							_lowerCamelCase     :			Tuple    =    config
							# download_callbacks take a single url as input
							_lowerCamelCase     :			List[Callable]    =    download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							_lowerCamelCase     :			Any    =    load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							_lowerCamelCase     :			str    =    str(lowercase  )
							# to be downloaded
							_lowerCamelCase     :			Union[str, Any]    =    None
							_lowerCamelCase     :			int    =    None
			@property
			def 					A_    (  self  ):
							if self._dummy_file is None:
											_lowerCamelCase     :			Tuple    =    self.download_dummy_data()
							return self._dummy_file
			@property
			def 					A_    (  self  ):
							if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('dummy'  ,      self.config.name  ,      self.version_name  )
							# structure is dummy / version_name
							return os.path.join('dummy'  ,      self.version_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.dummy_data_folder  ,      'dummy_data.zip'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    (
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							_lowerCamelCase     :			int    =    cached_path(
							    lowercase  ,      cache_dir=self.cache_dir  ,      extract_compressed_file=lowercase  ,      force_extract=lowercase  )

							return os.path.join(lowercase  ,      self.dummy_file_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.datasets_scripts_dir  ,      self.dataset_name  ,      self.dummy_zip_file  )
			@property
			def 					A_    (  self  ):
							if self._bucket_url is None:
											_lowerCamelCase     :			List[Any]    =    hf_github_url(self.dataset_name  ,      self.dummy_zip_file.replace(os.sep  ,      '/'  )  )
							return self._bucket_url
			@property
			def 					A_    (  self  ):
							# return full path if its a dir
							if os.path.isdir(self.dummy_file  ):
											return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep  ,      '/'  ).split('/'  )[:-1]  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file
							else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(lowercase  ,      lowercase  ):
											return self.create_dummy_data_dict(lowercase  ,      lowercase  )
							elif isinstance(lowercase  ,      (list, tuple)  ):
											return self.create_dummy_data_list(lowercase  ,      lowercase  )
							else:
											return self.create_dummy_data_single(lowercase  ,      lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ,      **lowercase  ):
							return path
			def 					A_    (  self  ):
							return {}
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    {}
							for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
															if isinstance(lowercase  ,      lowercase  ):
																			for single_url in single_urls:
																							download_callback(lowercase  )
															else:
																			_lowerCamelCase     :			List[Any]    =    single_urls
																			download_callback(lowercase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(lowercase  ,      lowercase  ):
															_lowerCamelCase     :			List[Any]    =    [os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  ) for x in single_urls]
											else:
															_lowerCamelCase     :			Optional[int]    =    single_urls
															_lowerCamelCase     :			List[Any]    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  )
											_lowerCamelCase     :			int    =    value

							# make sure that values are unique
							if all(isinstance(lowercase  ,      lowercase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
							    dummy_data_dict.values()  ):
											# append key to value to make its name unique
											_lowerCamelCase     :			List[Any]    =    {key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    []
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							_lowerCamelCase     :			List[str]    =    all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'  ,      lowercase  )  ) for url in data_url  )
							_lowerCamelCase     :			int    =    all(
							    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'  ) for url in data_url  )
							if data_url and (is_tf_records or is_pubmed_records):
											_lowerCamelCase     :			List[str]    =    [data_url[0]] * len(lowercase  )
							for single_url in data_url:
											for download_callback in self.download_callbacks:
															download_callback(lowercase  )
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											_lowerCamelCase     :			str    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(single_url.split('/'  )[-1]  )  )
											dummy_data_list.append(lowercase  )
							return dummy_data_list
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							for download_callback in self.download_callbacks:
											download_callback(lowercase  )
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							_lowerCamelCase     :			Tuple    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(data_url.split('/'  )[-1]  )  )
							if os.path.exists(lowercase  ) or not self.load_existing_dummy_data:
											return value
							else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ,      lowercase  ):
							def _iter_archive_members(lowercase  ):
											# this preserves the order of the members inside the ZIP archive
											_lowerCamelCase     :			str    =    Path(self.dummy_file  ).parent
											_lowerCamelCase     :			Union[str, Any]    =    path.relative_to(lowercase  )
											with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
															_lowerCamelCase     :			List[str]    =    zip_file.namelist()
											for member in members:
															if member.startswith(relative_path.as_posix()  ):
																			yield dummy_parent_path.joinpath(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    Path(lowercase  )
							_lowerCamelCase     :			Dict    =    _iter_archive_members(lowercase  ) if self.use_local_dummy_data else path.rglob('*'  )
							for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('.', '__')  ):
															yield file_path.relative_to(lowercase  ).as_posix(), file_path.open('rb'  )






			def 					A_    (  self  ,      lowercase  ):
							if not isinstance(lowercase  ,      lowercase  ):
											_lowerCamelCase     :			List[str]    =    [paths]
							for path in paths:
											if os.path.isfile(lowercase  ):
															if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																			return
															yield path
											else:
															for dirpath, dirnames, filenames in os.walk(lowercase  ):
																			if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																							continue
																			dirnames.sort()
																			for filename in sorted(lowercase  ):
																							if filename.startswith(('.', '__')  ):
																											continue
																							yield os.path.join(lowercase  ,      lowercase  )