






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def        snake_case   (     A__			,A__		):
			assert isinstance(_lowerCamelCase			,_lowerCamelCase		)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"			,[False, True]		)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Tuple      =      tmp_path / "cache"
			UpperCAmelCase_  :				Any      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_  :				List[Any]      =      JsonDatasetReader(_lowerCamelCase			,cache_dir=_lowerCamelCase			,keep_in_memory=_lowerCamelCase		).read()
			_check_json_dataset(_lowerCamelCase			,_lowerCamelCase		)

@pytest.mark.parametrize(
    "features"			,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      tmp_path / "cache"
			UpperCAmelCase_  :				List[str]      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_  :				Optional[Any]      =      features.copy() if features else default_expected_features
			UpperCAmelCase_  :				int      =      (
			    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_  :				Any      =      JsonDatasetReader(_lowerCamelCase			,features=_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			_check_json_dataset(_lowerCamelCase			,_lowerCamelCase		)

@pytest.mark.parametrize(
    "features"			,[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      tmp_path / "cache"
			UpperCAmelCase_  :				Dict      =      {"col_3": "float64", "col_1": "string", "col_2": "int64"}
			UpperCAmelCase_  :				Optional[Any]      =      features.copy() if features else default_expected_features
			UpperCAmelCase_  :				str      =      (
			    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_  :				Any      =      JsonDatasetReader(_lowerCamelCase			,features=_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			assert isinstance(_lowerCamelCase			,_lowerCamelCase		)
			assert dataset.num_rows == 2
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_3", "col_1", "col_2"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      {"col_2": "int64", "col_3": "float64", "col_1": "string"}
			UpperCAmelCase_  :				int      =      features.copy()
			UpperCAmelCase_  :				Optional[Any]      =      (
			    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_  :				Union[str, Any]      =      tmp_path / "cache"
			UpperCAmelCase_  :				Dict      =      JsonDatasetReader(_lowerCamelCase			,features=_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			assert isinstance(_lowerCamelCase			,_lowerCamelCase		)
			assert dataset.num_rows == 2
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_2", "col_3", "col_1"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("split"			,[None, NamedSplit("train"		), "train", "test"]		)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      tmp_path / "cache"
			UpperCAmelCase_  :				Dict      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_  :				Optional[Any]      =      JsonDatasetReader(_lowerCamelCase			,cache_dir=_lowerCamelCase			,split=_lowerCamelCase		).read()
			_check_json_dataset(_lowerCamelCase			,_lowerCamelCase		)
			assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"			,[str, list]		)
def        snake_case   (     A__			,A__			,A__		):


			if issubclass(_lowerCamelCase			,_lowerCamelCase		):
						UpperCAmelCase_  :				List[Any]      =      jsonl_path
			elif issubclass(_lowerCamelCase			,_lowerCamelCase		):
						UpperCAmelCase_  :				Optional[int]      =      [jsonl_path]
			UpperCAmelCase_  :				Tuple      =      tmp_path / "cache"
			UpperCAmelCase_  :				Tuple      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_  :				Union[str, Any]      =      JsonDatasetReader(_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			_check_json_dataset(_lowerCamelCase			,_lowerCamelCase		)

def        snake_case   (     A__			,A__			,A__=("train",)		):
			assert isinstance(_lowerCamelCase			,_lowerCamelCase		)
			for split in splits:
						UpperCAmelCase_  :				List[str]      =      dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"			,[False, True]		)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      tmp_path / "cache"
			UpperCAmelCase_  :				Optional[int]      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_  :				Optional[int]      =      JsonDatasetReader({"train": jsonl_path}			,cache_dir=_lowerCamelCase			,keep_in_memory=_lowerCamelCase		).read()
			_check_json_datasetdict(_lowerCamelCase			,_lowerCamelCase		)

@pytest.mark.parametrize(
    "features"			,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				str      =      tmp_path / "cache"
			UpperCAmelCase_  :				List[Any]      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_  :				Any      =      features.copy() if features else default_expected_features
			UpperCAmelCase_  :				List[str]      =      (
			    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_  :				int      =      JsonDatasetReader({"train": jsonl_path}			,features=_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			_check_json_datasetdict(_lowerCamelCase			,_lowerCamelCase		)

@pytest.mark.parametrize("split"			,[None, NamedSplit("train"		), "train", "test"]		)
def        snake_case   (     A__			,A__			,A__		):


			if split:
						UpperCAmelCase_  :				int      =      {split: jsonl_path}
			else:
						UpperCAmelCase_  :				Optional[Any]      =      "train"
						UpperCAmelCase_  :				Dict      =      {"train": jsonl_path, "test": jsonl_path}
			UpperCAmelCase_  :				Union[str, Any]      =      tmp_path / "cache"
			UpperCAmelCase_  :				Optional[int]      =      {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_  :				int      =      JsonDatasetReader(_lowerCamelCase			,cache_dir=_lowerCamelCase		).read()
			_check_json_datasetdict(_lowerCamelCase			,_lowerCamelCase			,splits=list(path.keys()		)		)
			assert all(dataset[split].split == split for split in path.keys()		)

def        snake_case   (     A__		):
			return json.load(_lowerCamelCase		)

def        snake_case   (     A__		):
			return [json.loads(_lowerCamelCase		) for line in buffer]







class     UpperCamelCase_ :





	@pytest.mark.parametrize("lines, load_json_function"			,			[(True, load_json_lines), (False, load_json)]		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Optional[int]		)		->    Optional[Any]:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			lines=lowerCAmelCase_		).write()
							buffer.seek(0		)
							UpperCAmelCase_  :				str      =      load_json_function(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_			,			lowerCAmelCase_		)
				assert isinstance(exported_content[0]			,			lowerCAmelCase_		)
				assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize(
	    "orient, container, keys, len_at"			,			[
	        ("records", list, {"tokens", "labels", "answers", "id"}, None),
	        ("split", dict, {"columns", "data"}, "data"),
	        ("index", dict, set("0123456789"		), None),
	        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
	        ("values", list, None, None),
	        ("table", dict, {"schema", "data"}, "data"),
	    ]			,			)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				List[str]		)		->    str:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			lines=lowerCAmelCase_			,			orient=lowerCAmelCase_		).write()
							buffer.seek(0		)
							UpperCAmelCase_  :				Union[str, Any]      =      load_json(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_			,			lowerCAmelCase_		)
				if keys:
							if container is dict:
										assert exported_content.keys() == keys
							else:
										assert exported_content[0].keys() == keys
				else:
							assert not hasattr(lowerCAmelCase_			,			"keys"		) and not hasattr(exported_content[0]			,			"keys"		)
				if len_at:
							assert len(exported_content[len_at]		) == 10
				else:
							assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize("lines, load_json_function"			,			[(True, load_json_lines), (False, load_json)]		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				str		)		->    Optional[Any]:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			lines=lowerCAmelCase_			,			num_proc=2		).write()
							buffer.seek(0		)
							UpperCAmelCase_  :				List[Any]      =      load_json_function(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_			,			lowerCAmelCase_		)
				assert isinstance(exported_content[0]			,			lowerCAmelCase_		)
				assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize(
	    "orient, container, keys, len_at"			,			[
	        ("records", list, {"tokens", "labels", "answers", "id"}, None),
	        ("split", dict, {"columns", "data"}, "data"),
	        ("index", dict, set("0123456789"		), None),
	        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
	        ("values", list, None, None),
	        ("table", dict, {"schema", "data"}, "data"),
	    ]			,			)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[Any]		)		->    Any:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			lines=lowerCAmelCase_			,			orient=lowerCAmelCase_			,			num_proc=2		).write()
							buffer.seek(0		)
							UpperCAmelCase_  :				Dict      =      load_json(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_			,			lowerCAmelCase_		)
				if keys:
							if container is dict:
										assert exported_content.keys() == keys
							else:
										assert exported_content[0].keys() == keys
				else:
							assert not hasattr(lowerCAmelCase_			,			"keys"		) and not hasattr(exported_content[0]			,			"keys"		)
				if len_at:
							assert len(exported_content[len_at]		) == 10
				else:
							assert len(lowerCAmelCase_		) == 10





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Dict		)		->    Optional[Any]:
				with pytest.raises(lowerCAmelCase_		):
							with io.BytesIO() as buffer:
										JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			num_proc=0		)





	@pytest.mark.parametrize("compression, extension"			,			[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				Dict      =      tmp_path_factory.mktemp("data"		) / f"""test.json.{extension}"""
				UpperCAmelCase_  :				List[str]      =      str(shared_datadir / f"""test_file.json.{extension}"""		)
				JsonDatasetWriter(lowerCAmelCase_			,			lowerCAmelCase_			,			compression=lowerCAmelCase_		).write()

				with fsspec.open(lowerCAmelCase_			,			"rb"			,			compression="infer"		) as f:
							UpperCAmelCase_  :				Dict      =      f.read()
				with fsspec.open(lowerCAmelCase_			,			"rb"			,			compression="infer"		) as f:
							UpperCAmelCase_  :				Any      =      f.read()
				assert exported_content == original_content











"""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()



