

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     (				a_				:					Union[str, Any]					,a_				:					str					,a_				:					int=[]				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    size[0] - overlap_pixels * 2
					__magic_name__							=    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
					__magic_name__							=    np.ones((size_y, size_x)					,dtype=np.uinta				) * 2_5_5
					__magic_name__							=    np.pad(a_					,mode='linear_ramp'					,pad_width=a_					,end_values=0				)

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




def 					_lowercase     (				a_				:					Any					,a_				:					Tuple					,a_				:					Dict				)				->	Dict:




					'''simple docstring'''







					return max(a_					,min(a_					,a_				)				)




def 					_lowercase     (				a_				:					[int]					,a_				:					[int]					,a_				:					[int]				)				->	int:




					'''simple docstring'''







					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     (				a_				:					[int]					,a_				:					int					,a_				:					[int]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    list(a_				)
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					__magic_name__							=    clamp_rect(a_					,[0, 0]					,[image_size[0], image_size[1]]				)
					return rect




def 					_lowercase     (				a_				:					Any					,a_				:					str					,a_				:					Optional[Any]					,a_				:					Optional[int]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    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 					_lowercase     (				a_				:					Dict					,a_				:					Any				)				->	str:




					'''simple docstring'''







					__magic_name__							=    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					__magic_name__							=    tile.crop(a_				)
					return tile




def 					_lowercase     (				a_				:					Tuple					,a_				:					Optional[int]				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    n % d
					return n - divisor

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: Dict  , __UpperCamelCase: AutoencoderKL  , __UpperCamelCase: CLIPTextModel  , __UpperCamelCase: CLIPTokenizer  , __UpperCamelCase: UNetaDConditionModel  , __UpperCamelCase: DDPMScheduler  , __UpperCamelCase: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  , __UpperCamelCase: int = 3_50  , ):

						'''simple docstring'''





						super().__init__(
						    vae=__UpperCamelCase  , text_encoder=__UpperCamelCase  , tokenizer=__UpperCamelCase  , unet=__UpperCamelCase  , low_res_scheduler=__UpperCamelCase  , scheduler=__UpperCamelCase  , max_noise_level=__UpperCamelCase  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: str  , __UpperCamelCase: Tuple  , __UpperCamelCase: str  , __UpperCamelCase: int  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: List[str]  , __UpperCamelCase: List[str]  , **__UpperCamelCase: str      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    (
						    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      ),
						)
						__magic_name__							=    add_overlap_rect(__UpperCamelCase  , __UpperCamelCase  , image.size      )
						__magic_name__							=    image.crop(__UpperCamelCase      )
						__magic_name__							=    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
						__magic_name__							=    translated_slice_x - (original_image_slice / 2)
						__magic_name__							=    max(0  , __UpperCamelCase      )
						__magic_name__							=    squeeze_tile(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    to_input.size
						__magic_name__							=    to_input.resize((tile_size, tile_size)  , Image.BICUBIC      )
						__magic_name__							=    super(__UpperCamelCase  , self      ).__call__(image=__UpperCamelCase  , **__UpperCamelCase      ).images[0]
						__magic_name__							=    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)  , Image.BICUBIC      )
						__magic_name__							=    unsqueeze_tile(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)  , Image.BICUBIC      )
						__magic_name__							=    []
						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'      )
						__magic_name__							=    Image.fromarray(
						    make_transparency_mask(
						        (upscaled_tile.size[0], upscaled_tile.size[1])  , tile_border * 4  , remove_borders=__UpperCamelCase      )  , mode='L'  , )
						final_image.paste(
						    __UpperCamelCase  , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)  , __UpperCamelCase      )







	@torch.no_grad()
	def __call__(   self: Union[str, Any]  , __UpperCamelCase: Union[str, List[str]]  , __UpperCamelCase: Union[PIL.Image.Image, List[PIL.Image.Image]]  , __UpperCamelCase: int = 75  , __UpperCamelCase: float = 9.0  , __UpperCamelCase: int = 50  , __UpperCamelCase: Optional[Union[str, List[str]]] = None  , __UpperCamelCase: Optional[int] = 1  , __UpperCamelCase: float = 0.0  , __UpperCamelCase: Optional[torch.Generator] = None  , __UpperCamelCase: Optional[torch.FloatTensor] = None  , __UpperCamelCase: Optional[Callable[[int, int, torch.FloatTensor], None]] = None  , __UpperCamelCase: int = 1  , __UpperCamelCase: int = 1_28  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 32  , ):

						'''simple docstring'''





						__magic_name__							=    Image.new('RGB'  , (image.size[0] * 4, image.size[1] * 4)      )
						__magic_name__							=    math.ceil(image.size[0] / tile_size      )
						__magic_name__							=    math.ceil(image.size[1] / tile_size      )
						__magic_name__							=    tcx * tcy
						__magic_name__							=    0
						for y in range(__UpperCamelCase      ):
											for x in range(__UpperCamelCase      ):
																self._process_tile(
																    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , prompt=__UpperCamelCase  , num_inference_steps=__UpperCamelCase  , guidance_scale=__UpperCamelCase  , noise_level=__UpperCamelCase  , negative_prompt=__UpperCamelCase  , num_images_per_prompt=__UpperCamelCase  , eta=__UpperCamelCase  , generator=__UpperCamelCase  , latents=__UpperCamelCase  , )
																current_count += 1
																if callback is not None:
																					callback({'progress': current_count / total_tile_count, 'image': final_image}      )
						return final_image




def 					_lowercase     (				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    'stabilityai/stable-diffusion-x4-upscaler'
					__magic_name__							=    StableDiffusionTiledUpscalePipeline.from_pretrained(a_					,revision='fp16'					,torch_dtype=torch.floataa				)
					__magic_name__							=    pipe.to('cuda'				)
					__magic_name__							=    Image.open('../../docs/source/imgs/diffusers_library.jpg'				)

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

					__magic_name__							=    pipe(image=a_					,prompt='Black font, white background, vector'					,noise_level=4_0					,callback=a_				)
					final_image.save('diffusers_library.jpg'				)


if __name__ == "__main__":
						main()







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A__						=							logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   Optional[str]       				=  field(
	    default="cifar10"  ,						metadata={"help": "Name of a dataset from the datasets package"}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "The column name of the images in the files."}			)
	_lowercase  :   Optional[str]       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "A folder containing the training data."}			)
	_lowercase  :   Optional[str]       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "A folder containing the validation data."}			)
	_lowercase  :   Optional[float]       				=  field(
	    default=0.15  ,						metadata={"help": "Percent to split off of train for validation."}			)
	_lowercase  :   Optional[int]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }  ,						)
	_lowercase  :   Optional[int]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }  ,						)







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    {}
						if self.train_dir is not None:
											__magic_name__							=    self.train_dir
						if self.validation_dir is not None:
											__magic_name__							=    self.validation_dir
						__magic_name__							=    data_files if data_files else None




@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   str       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    }  ,						)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}			)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "Override some existing default config settings when a model is trained from scratch. Example: "
	            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
	        )
	    }  ,						)
	_lowercase  :   Optional[str]       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}			)
	_lowercase  :   str       				=  field(
	    default="main"  ,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}  ,						)
	_lowercase  :   str       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Name or path of preprocessor config."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }  ,						)
	_lowercase  :   float       				=  field(
	    default=0.75  ,						metadata={"help": "The ratio of the number of masked tokens in the input sequence."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether or not to train with normalized pixel values as target."}			)




@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   float       				=  field(
	    default=1E-3  ,						metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}			)




def 					_lowercase     (				a_				:					str				)				->	Tuple:




					'''simple docstring'''







					__magic_name__							=    torch.stack([example['pixel_values'] for example in examples]				)
					return {"pixel_values": pixel_values}




def 					_lowercase     (				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
					if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__magic_name__,	__magic_name__,	__magic_name__							=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
					else:
										__magic_name__,	__magic_name__,	__magic_name__							=    parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry('run_mae'					,a_					,a_				)

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,datefmt='%m/%d/%Y %H:%M:%S'					,handlers=[logging.StreamHandler(sys.stdout				)]					,)

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					__magic_name__							=    training_args.get_process_log_level()
					logger.setLevel(a_				)
					transformers.utils.logging.set_verbosity(a_				)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
					    + F'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
					logger.info(F'Training/evaluation parameters {training_args}'				)

					# Detecting last checkpoint.
					__magic_name__							=    None
					if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
										__magic_name__							=    get_last_checkpoint(training_args.output_dir				)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
															raise ValueError(
															    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
															    'Use --overwrite_output_dir to overcome.'				)
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
															    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Initialize our dataset.
					__magic_name__							=    load_dataset(
					    data_args.dataset_name					,data_args.dataset_config_name					,data_files=data_args.data_files					,cache_dir=model_args.cache_dir					,use_auth_token=True if model_args.use_auth_token else None					,)

					# If we don't have a validation split, split off a percentage of train as validation.
					__magic_name__							=    None if 'validation' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split					,a_				) and data_args.train_val_split > 0.0:
										__magic_name__							=    ds['train'].train_test_split(data_args.train_val_split				)
										__magic_name__							=    split['train']
										__magic_name__							=    split['test']

					# Load pretrained model and image processor
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					__magic_name__							=    {
					    'cache_dir': model_args.cache_dir,
					    'revision': model_args.model_revision,
					    'use_auth_token': True if model_args.use_auth_token else None,
					}
					if model_args.config_name:
										__magic_name__							=    ViTMAEConfig.from_pretrained(model_args.config_name					,**a_				)
					elif model_args.model_name_or_path:
										__magic_name__							=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,**a_				)
					else:
										__magic_name__							=    ViTMAEConfig()
										logger.warning('You are instantiating a new config instance from scratch.'				)
										if model_args.config_overrides is not None:
															logger.info(F'Overriding config: {model_args.config_overrides}'				)
															config.update_from_string(model_args.config_overrides				)
															logger.info(F'New config: {config}'				)

    # adapt config
					config.update(
					    {
					        'mask_ratio': model_args.mask_ratio,
					        'norm_pix_loss': model_args.norm_pix_loss,
					    }				)

					# create image processor
					if model_args.image_processor_name:
										__magic_name__							=    ViTImageProcessor.from_pretrained(model_args.image_processor_name					,**a_				)
					elif model_args.model_name_or_path:
										__magic_name__							=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,**a_				)
					else:
										__magic_name__							=    ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										__magic_name__							=    ViTMAEForPreTraining.from_pretrained(
										    model_args.model_name_or_path					,from_tf=bool('.ckpt' in model_args.model_name_or_path				)					,config=a_					,cache_dir=model_args.cache_dir					,revision=model_args.model_revision					,use_auth_token=True if model_args.use_auth_token else None					,)
					else:
										logger.info('Training new model from scratch'				)
										__magic_name__							=    ViTMAEForPreTraining(a_				)

					if training_args.do_train:
										__magic_name__							=    ds['train'].column_names
					else:
										__magic_name__							=    ds['validation'].column_names

					if data_args.image_column_name is not None:
										__magic_name__							=    data_args.image_column_name
					elif "image" in column_names:
										__magic_name__							=    'image'
					elif "img" in column_names:
										__magic_name__							=    'img'
					else:
										__magic_name__							=    column_names[0]

					# transformations as done in original MAE paper
					# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
					if "shortest_edge" in image_processor.size:
										__magic_name__							=    image_processor.size['shortest_edge']
					else:
										__magic_name__							=    (image_processor.size['height'], image_processor.size['width'])
					__magic_name__							=    Compose(
					    [
					        Lambda(lambda a_				: img.convert('RGB'				) if img.mode != "RGB" else img				),
					        RandomResizedCrop(a_					,scale=(0.2, 1.0)					,interpolation=InterpolationMode.BICUBIC				),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean					,std=image_processor.image_std				),
					    ]				)

					def preprocess_images(a_				:					Dict				):
										__magic_name__							=    [transforms(a_				) for image in examples[image_column_name]]
										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError('--do_train requires a train dataset'				)
										if data_args.max_train_samples is not None:
															__magic_name__							=    ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
										# Set the training transforms
										ds["train"].set_transform(a_				)

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError('--do_eval requires a validation dataset'				)
										if data_args.max_eval_samples is not None:
															__magic_name__							=    (
															    ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
															)
										# Set the validation transforms
										ds["validation"].set_transform(a_				)

					# Compute absolute learning rate
					__magic_name__							=    (
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										__magic_name__							=    training_args.base_learning_rate * total_train_batch_size / 2_5_6

					# Initialize our trainer
					__magic_name__							=    Trainer(
					    model=a_					,args=a_					,train_dataset=ds['train'] if training_args.do_train else None					,eval_dataset=ds['validation'] if training_args.do_eval else None					,tokenizer=a_					,data_collator=a_					,)

					# Training
					if training_args.do_train:
										__magic_name__							=    None
										if training_args.resume_from_checkpoint is not None:
															__magic_name__							=    training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__magic_name__							=    last_checkpoint
										__magic_name__							=    trainer.train(resume_from_checkpoint=a_				)
										trainer.save_model()
										trainer.log_metrics('train'					,train_result.metrics				)
										trainer.save_metrics('train'					,train_result.metrics				)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										__magic_name__							=    trainer.evaluate()
										trainer.log_metrics('eval'					,a_				)
										trainer.save_metrics('eval'					,a_				)

					# Write model card and (optionally) push to hub
					__magic_name__							=    {
					    'tasks': 'masked-auto-encoding',
					    'dataset': data_args.dataset_name,
					    'tags': ['masked-auto-encoding'],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**a_				)
					else:
										trainer.create_model_card(**a_				)




def 					_lowercase     (				a_				:					str				)				->	Tuple:




					'''simple docstring'''







					main()


if __name__ == "__main__":
						main()




