

import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_SCREAMING_SNAKE_CASE  :						int         =    {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
     assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
     if args.mlm:
          assert os.path.isfile(args.token_counts       )
          assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
     else:
          assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

     assert args.teacher_type == args.student_type or (
         args.student_type == "distilbert" and args.teacher_type == "bert"
     )
     assert os.path.isfile(args.student_config       )
     if args.student_pretrained_weights is not None:
          assert os.path.isfile(args.student_pretrained_weights       )

     if args.freeze_token_type_embds:
          assert args.student_type in ["roberta"]

     assert args.alpha_ce >= 0.0
     assert args.alpha_mlm >= 0.0
     assert args.alpha_clm >= 0.0
     assert args.alpha_mse >= 0.0
     assert args.alpha_cos >= 0.0
     assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if args.student_type == "roberta":
          SCREAMING_SNAKE_CASE__					=					False
     elif args.student_type == "gpt2":
          SCREAMING_SNAKE_CASE__					=					False
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if args.student_type == "roberta":
          SCREAMING_SNAKE_CASE__					=					False
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser(description='''Training'''       )
     parser.add_argument('''--force'''   ,	action='''store_true'''   ,	help='''Overwrite dump_path if it already exists.'''       )

     parser.add_argument(
         '''--dump_path'''   ,	type=_A   ,	required=_A   ,	help='''The output directory (log, checkpoints, parameters, etc.)'''       )
     parser.add_argument(
         '''--data_file'''   ,	type=_A   ,	required=_A   ,	help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   ,	)

     parser.add_argument(
         '''--student_type'''   ,	type=_A   ,	choices=['''distilbert''', '''roberta''', '''gpt2''']   ,	required=_A   ,	help='''The student type (DistilBERT, RoBERTa).'''   ,	)
     parser.add_argument('''--student_config'''   ,	type=_A   ,	required=_A   ,	help='''Path to the student configuration.'''       )
     parser.add_argument(
         '''--student_pretrained_weights'''   ,	default=_A   ,	type=_A   ,	help='''Load student initialization checkpoint.'''       )

     parser.add_argument(
         '''--teacher_type'''   ,	choices=['''bert''', '''roberta''', '''gpt2''']   ,	required=_A   ,	help='''Teacher type (BERT, RoBERTa).'''       )
     parser.add_argument('''--teacher_name'''   ,	type=_A   ,	required=_A   ,	help='''The teacher model.'''       )

     parser.add_argument('''--temperature'''   ,	default=2.0   ,	type=_A   ,	help='''Temperature for the softmax temperature.'''       )
     parser.add_argument(
         '''--alpha_ce'''   ,	default=0.5   ,	type=_A   ,	help='''Linear weight for the distillation loss. Must be >=0.'''       )
     parser.add_argument(
         '''--alpha_mlm'''   ,	default=0.0   ,	type=_A   ,	help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''   ,	)
     parser.add_argument('''--alpha_clm'''   ,	default=0.5   ,	type=_A   ,	help='''Linear weight for the CLM loss. Must be >=0.'''       )
     parser.add_argument('''--alpha_mse'''   ,	default=0.0   ,	type=_A   ,	help='''Linear weight of the MSE loss. Must be >=0.'''       )
     parser.add_argument(
         '''--alpha_cos'''   ,	default=0.0   ,	type=_A   ,	help='''Linear weight of the cosine embedding loss. Must be >=0.'''       )

     parser.add_argument(
         '''--mlm'''   ,	action='''store_true'''   ,	help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''       )
     parser.add_argument(
         '''--mlm_mask_prop'''   ,	default=0.1_5   ,	type=_A   ,	help='''Proportion of tokens for which we need to make a prediction.'''   ,	)
     parser.add_argument('''--word_mask'''   ,	default=0.8   ,	type=_A   ,	help='''Proportion of tokens to mask out.'''       )
     parser.add_argument('''--word_keep'''   ,	default=0.1   ,	type=_A   ,	help='''Proportion of tokens to keep.'''       )
     parser.add_argument('''--word_rand'''   ,	default=0.1   ,	type=_A   ,	help='''Proportion of tokens to randomly replace.'''       )
     parser.add_argument(
         '''--mlm_smoothing'''   ,	default=0.7   ,	type=_A   ,	help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   ,	)
     parser.add_argument('''--token_counts'''   ,	type=_A   ,	help='''The token counts in the data_file for MLM.'''       )

     parser.add_argument(
         '''--restrict_ce_to_mask'''   ,	action='''store_true'''   ,	help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''   ,	)
     parser.add_argument(
         '''--freeze_pos_embs'''   ,	action='''store_true'''   ,	help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''   ,	)
     parser.add_argument(
         '''--freeze_token_type_embds'''   ,	action='''store_true'''   ,	help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''   ,	)

     parser.add_argument('''--n_epoch'''   ,	type=_A   ,	default=3   ,	help='''Number of pass on the whole dataset.'''       )
     parser.add_argument('''--batch_size'''   ,	type=_A   ,	default=5   ,	help='''Batch size (for each process).'''       )
     parser.add_argument(
         '''--group_by_size'''   ,	action='''store_false'''   ,	help='''If true, group sequences that have similar length into the same batch. Default is true.'''   ,	)

     parser.add_argument(
         '''--gradient_accumulation_steps'''   ,	type=_A   ,	default=50   ,	help='''Gradient accumulation for larger training batches.'''   ,	)
     parser.add_argument('''--warmup_prop'''   ,	default=0.0_5   ,	type=_A   ,	help='''Linear warmup proportion.'''       )
     parser.add_argument('''--weight_decay'''   ,	default=0.0   ,	type=_A   ,	help='''Weight decay if we apply some.'''       )
     parser.add_argument('''--learning_rate'''   ,	default=5e-4   ,	type=_A   ,	help='''The initial learning rate for Adam.'''       )
     parser.add_argument('''--adam_epsilon'''   ,	default=1e-6   ,	type=_A   ,	help='''Epsilon for Adam optimizer.'''       )
     parser.add_argument('''--max_grad_norm'''   ,	default=5.0   ,	type=_A   ,	help='''Max gradient norm.'''       )
     parser.add_argument('''--initializer_range'''   ,	default=0.0_2   ,	type=_A   ,	help='''Random initialization range.'''       )

     parser.add_argument(
         '''--fp16'''   ,	action='''store_true'''   ,	help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,	)
     parser.add_argument(
         '''--fp16_opt_level'''   ,	type=_A   ,	default='''O1'''   ,	help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )   ,	)
     parser.add_argument('''--n_gpu'''   ,	type=_A   ,	default=1   ,	help='''Number of GPUs in the node.'''       )
     parser.add_argument('''--local_rank'''   ,	type=_A   ,	default=-1   ,	help='''Distributed training - Local rank'''       )
     parser.add_argument('''--seed'''   ,	type=_A   ,	default=56   ,	help='''Random seed'''       )

     parser.add_argument('''--log_interval'''   ,	type=_A   ,	default=5_00   ,	help='''Tensorboard logging interval.'''       )
     parser.add_argument('''--checkpoint_interval'''   ,	type=_A   ,	default=40_00   ,	help='''Checkpoint interval.'''       )
     SCREAMING_SNAKE_CASE__					=					parser.parse_args()
     sanity_checks(_A       )

     # ARGS #
     init_gpu_params(_A       )
     set_seed(_A       )
     if args.is_master:
          if os.path.exists(args.dump_path       ):
               if not args.force:
                    raise ValueError(
                        F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
                        ''' itUse `--force` if you want to overwrite it'''       )
               else:
                    shutil.rmtree(args.dump_path       )

          if not os.path.exists(args.dump_path       ):
               os.makedirs(args.dump_path       )
          logger.info(F'''Experiment will be dumped and logged in {args.dump_path}'''       )

          # SAVE PARAMS #
          logger.info(F'''Param: {args}'''       )
          with open(os.path.join(args.dump_path   ,	'''parameters.json'''       )   ,	'''w'''       ) as f:
               json.dump(vars(_A       )   ,	_A   ,	indent=4       )
          git_log(args.dump_path       )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					MODEL_CLASSES[args.student_type]
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					MODEL_CLASSES[args.teacher_type]

     # TOKENIZER #
     SCREAMING_SNAKE_CASE__					=					teacher_tokenizer_class.from_pretrained(args.teacher_name       )
     SCREAMING_SNAKE_CASE__					=					{}
     for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
          SCREAMING_SNAKE_CASE__					=					tokenizer.all_special_tokens.index(_A       )
          SCREAMING_SNAKE_CASE__					=					tokenizer.all_special_ids[idx]
     logger.info(F'''Special tokens {special_tok_ids}'''       )
     SCREAMING_SNAKE_CASE__					=					special_tok_ids
     SCREAMING_SNAKE_CASE__					=					tokenizer.max_model_input_sizes[args.teacher_name]

     # DATA LOADER #
     logger.info(F'''Loading data from {args.data_file}'''       )
     with open(args.data_file   ,	'''rb'''       ) as fp:
          SCREAMING_SNAKE_CASE__					=					pickle.load(_A       )

     if args.mlm:
          logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''       )
          with open(args.token_counts   ,	'''rb'''       ) as fp:
               SCREAMING_SNAKE_CASE__					=					pickle.load(_A       )

          SCREAMING_SNAKE_CASE__					=					np.maximum(_A   ,	1       ) ** -args.mlm_smoothing
          for idx in special_tok_ids.values():
               SCREAMING_SNAKE_CASE__					=					0.0  # do not predict special tokens
          SCREAMING_SNAKE_CASE__					=					torch.from_numpy(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					None

     SCREAMING_SNAKE_CASE__					=					LmSeqsDataset(params=_A   ,	data=_A       )
     logger.info('''Data loader created.'''       )

     # STUDENT #
     logger.info(F'''Loading student config from {args.student_config}'''       )
     SCREAMING_SNAKE_CASE__					=					student_config_class.from_pretrained(args.student_config       )
     SCREAMING_SNAKE_CASE__					=					True

     if args.student_pretrained_weights is not None:
          logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''       )
          SCREAMING_SNAKE_CASE__					=					student_model_class.from_pretrained(args.student_pretrained_weights   ,	config=_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					student_model_class(_A       )

     if args.n_gpu > 0:
          student.to(F'''cuda:{args.local_rank}'''       )
     logger.info('''Student loaded.'''       )

     # TEACHER #
     SCREAMING_SNAKE_CASE__					=					teacher_model_class.from_pretrained(args.teacher_name   ,	output_hidden_states=_A       )
     if args.n_gpu > 0:
          teacher.to(F'''cuda:{args.local_rank}'''       )
     logger.info(F'''Teacher loaded from {args.teacher_name}.'''       )

     # FREEZING #
     if args.freeze_pos_embs:
          freeze_pos_embeddings(_A   ,	_A       )
     if args.freeze_token_type_embds:
          freeze_token_type_embeddings(_A   ,	_A       )

     # SANITY CHECKS #
     assert student.config.vocab_size == teacher.config.vocab_size
     assert student.config.hidden_size == teacher.config.hidden_size
     assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
     if args.mlm:
          assert token_probs.size(0       ) == stu_architecture_config.vocab_size

     # DISTILLER #
     torch.cuda.empty_cache()
     SCREAMING_SNAKE_CASE__					=					Distiller(
         params=_A   ,	dataset=_A   ,	token_probs=_A   ,	student=_A   ,	teacher=_A       )
     distiller.train()
     logger.info('''Let\'s go get some drinks.'''       )


if __name__ == "__main__":
       main()





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        UpperCAmelCase_     (		_A   ,	_A   ,	_A=[]       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					size[0] - overlap_pixels * 2
     SCREAMING_SNAKE_CASE__					=					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__					=					np.ones((size_y, size_x)   ,	dtype=np.uinta       ) * 2_55
     SCREAMING_SNAKE_CASE__					=					np.pad(_A   ,	mode='''linear_ramp'''   ,	pad_width=_A   ,	end_values=0       )

     if "l" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[:, overlap_pixels : mask.shape[1]]
     if "r" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[:, 0 : mask.shape[1] - overlap_pixels]
     if "t" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[overlap_pixels : mask.shape[0], :]
     if "b" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[0 : mask.shape[0] - overlap_pixels, :]
     return mask
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     return max(_A   ,	min(_A   ,	_A       )       )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''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        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					list(_A       )
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     SCREAMING_SNAKE_CASE__					=					clamp_rect(_A   ,	[0, 0]   ,	[image_size[0], image_size[1]]       )
     return rect
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     SCREAMING_SNAKE_CASE__					=					tile.crop(_A       )
     return tile
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					n % d
     return n - divisor






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


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






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     **__lowerCamelCase		:	Tuple      )	->							Dict:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					(
           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__					=					add_overlap_rect(__lowerCamelCase			,     __lowerCamelCase			,     image.size      )
       SCREAMING_SNAKE_CASE__					=					image.crop(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       SCREAMING_SNAKE_CASE__					=					translated_slice_x - (original_image_slice / 2)
       SCREAMING_SNAKE_CASE__					=					max(0			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					squeeze_tile(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					to_input.size
       SCREAMING_SNAKE_CASE__					=					to_input.resize((tile_size, tile_size)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					super(__lowerCamelCase			,     self      ).__call__(image=__lowerCamelCase			,     **__lowerCamelCase      ).images[0]
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					unsqueeze_tile(__lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					[]
       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__					=					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		:	List[str]			,     __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			,     )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					Image.new('''RGB'''			,     (image.size[0] * 4, image.size[1] * 4)      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[0] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[1] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					tcx * tcy
       SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		):





     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE__					=					pipe(image=_A   ,	prompt='''Black font, white background, vector'''   ,	noise_level=40   ,	callback=_A       )
     final_image.save('''diffusers_library.jpg'''       )


if __name__ == "__main__":
       main()


