

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE  :						List[str]         =    logging.getLogger(__name__)






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      42
  a     =      42






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      42
  a     =      None
  a     =      None






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "train"
  a     =      "dev"
  a     =      "test"






class  UpperCAmelCase__	:






  """simple docstring"""


  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Union[Split, str]      )	->							List[InputExample]:
       raise NotImplementedError






  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	str      )	->							List[str]:
       raise NotImplementedError







  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	List[InputExample]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	Tuple=False			,     __lowerCamelCase		:	Tuple="[CLS]"			,     __lowerCamelCase		:	Dict=1			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	Optional[int]=False			,     __lowerCamelCase		:	Any=False			,     __lowerCamelCase		:	List[Any]=0			,     __lowerCamelCase		:	int=0			,     __lowerCamelCase		:	str=-100			,     __lowerCamelCase		:	Tuple=0			,     __lowerCamelCase		:	Tuple=True			,     )	->							List[InputFeatures]:
       SCREAMING_SNAKE_CASE__					=					{label: i for i, label in enumerate(__lowerCamelCase      )}

       SCREAMING_SNAKE_CASE__					=					[]
       for ex_index, example in enumerate(__lowerCamelCase      ):
            if ex_index % 1_0000 == 0:
                 logger.info('''Writing example %d of %d'''			,     __lowerCamelCase			,     len(__lowerCamelCase      )      )

            SCREAMING_SNAKE_CASE__					=					[]
            SCREAMING_SNAKE_CASE__					=					[]
            for word, label in zip(example.words			,     example.labels      ):
                 SCREAMING_SNAKE_CASE__					=					tokenizer.tokenize(__lowerCamelCase      )

                 # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                 if len(__lowerCamelCase      ) > 0:
                      tokens.extend(__lowerCamelCase      )
                      # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                      label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowerCamelCase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
            SCREAMING_SNAKE_CASE__					=					tokenizer.num_special_tokens_to_add()
            if len(__lowerCamelCase      ) > max_seq_length - special_tokens_count:
                 SCREAMING_SNAKE_CASE__					=					tokens[: (max_seq_length - special_tokens_count)]
                 SCREAMING_SNAKE_CASE__					=					label_ids[: (max_seq_length - special_tokens_count)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids:   0   0   0   0  0     0   0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
            if sep_token_extra:
                 # roberta uses an extra separator b/w pairs of sentences
                 tokens += [sep_token]
                 label_ids += [pad_token_label_id]
            SCREAMING_SNAKE_CASE__					=					[sequence_a_segment_id] * len(__lowerCamelCase      )

            if cls_token_at_end:
                 tokens += [cls_token]
                 label_ids += [pad_token_label_id]
                 segment_ids += [cls_token_segment_id]
            else:
                 SCREAMING_SNAKE_CASE__					=					[cls_token] + tokens
                 SCREAMING_SNAKE_CASE__					=					[pad_token_label_id] + label_ids
                 SCREAMING_SNAKE_CASE__					=					[cls_token_segment_id] + segment_ids

            SCREAMING_SNAKE_CASE__					=					tokenizer.convert_tokens_to_ids(__lowerCamelCase      )

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            SCREAMING_SNAKE_CASE__					=					[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase      )

            # Zero-pad up to the sequence length.
            SCREAMING_SNAKE_CASE__					=					max_seq_length - len(__lowerCamelCase      )
            if pad_on_left:
                 SCREAMING_SNAKE_CASE__					=					([pad_token] * padding_length) + input_ids
                 SCREAMING_SNAKE_CASE__					=					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                 SCREAMING_SNAKE_CASE__					=					([pad_token_segment_id] * padding_length) + segment_ids
                 SCREAMING_SNAKE_CASE__					=					([pad_token_label_id] * padding_length) + label_ids
            else:
                 input_ids += [pad_token] * padding_length
                 input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                 segment_ids += [pad_token_segment_id] * padding_length
                 label_ids += [pad_token_label_id] * padding_length

            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length

            if ex_index < 5:
                 logger.info('''*** Example ***'''      )
                 logger.info('''guid: %s'''			,     example.guid      )
                 logger.info('''tokens: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in tokens]      )      )
                 logger.info('''input_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in input_ids]      )      )
                 logger.info('''input_mask: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in input_mask]      )      )
                 logger.info('''segment_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in segment_ids]      )      )
                 logger.info('''label_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in label_ids]      )      )

            if "token_type_ids" not in tokenizer.model_input_names:
                 SCREAMING_SNAKE_CASE__					=					None

            features.append(
                InputFeatures(
                    input_ids=__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     label_ids=__lowerCamelCase      )      )
       return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset






       class  UpperCAmelCase__	( A__ ):






         """simple docstring"""


         a     =      42
         a     =      nn.CrossEntropyLoss().ignore_index






         def __init__(							self		:	str			,     __lowerCamelCase		:	TokenClassificationTask			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	Split = Split.train			,     )	->							List[str]:
              # Load data features from cache or dataset file
              SCREAMING_SNAKE_CASE__					=					os.path.join(
                  __lowerCamelCase			,     '''cached_{}_{}_{}'''.format(mode.value			,     tokenizer.__class__.__name__			,     str(__lowerCamelCase      )      )			,     )

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              SCREAMING_SNAKE_CASE__					=					cached_features_file + '''.lock'''
              with FileLock(__lowerCamelCase      ):
                   if os.path.exists(__lowerCamelCase      ) and not overwrite_cache:
                        logger.info(f'''Loading features from cached file {cached_features_file}'''      )
                        SCREAMING_SNAKE_CASE__					=					torch.load(__lowerCamelCase      )
                   else:
                        logger.info(f'''Creating features from dataset file at {data_dir}'''      )
                        SCREAMING_SNAKE_CASE__					=					token_classification_task.read_examples_from_file(__lowerCamelCase			,     __lowerCamelCase      )
                        # TODO clean up all this to leverage built-in features of tokenizers
                        SCREAMING_SNAKE_CASE__					=					token_classification_task.convert_examples_to_features(
                            __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     cls_token_at_end=bool(model_type in ['''xlnet''']      )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=__lowerCamelCase			,     pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )
                        logger.info(f'''Saving features into cached file {cached_features_file}'''      )
                        torch.save(self.features			,     __lowerCamelCase      )






         def __len__(							self		:	Tuple      )	->							Optional[int]:
              return len(self.features      )







         def __getitem__(							self		:	Any			,     __lowerCamelCase		:	Optional[Any]      )	->							InputFeatures:
              return self.features[i]


if is_tf_available():
       import tensorflow as tf






       class  UpperCAmelCase__	:






         """simple docstring"""


         a     =      42
         a     =      -1_00






         def __init__(							self		:	Union[str, Any]			,     __lowerCamelCase		:	TokenClassificationTask			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Optional[int]=False			,     __lowerCamelCase		:	Split = Split.train			,     )	->							Optional[Any]:
              SCREAMING_SNAKE_CASE__					=					token_classification_task.read_examples_from_file(__lowerCamelCase			,     __lowerCamelCase      )
              # TODO clean up all this to leverage built-in features of tokenizers
              SCREAMING_SNAKE_CASE__					=					token_classification_task.convert_examples_to_features(
                  __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     cls_token_at_end=bool(model_type in ['''xlnet''']      )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=__lowerCamelCase			,     pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )

              def gen():
                   for ex in self.features:
                        if ex.token_type_ids is None:
                             yield (
                                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                 ex.label_ids,
                             )
                        else:
                             yield (
                                 {
                                     "input_ids": ex.input_ids,
                                     "attention_mask": ex.attention_mask,
                                     "token_type_ids": ex.token_type_ids,
                                 },
                                 ex.label_ids,
                             )

              if "token_type_ids" not in tokenizer.model_input_names:
                   SCREAMING_SNAKE_CASE__					=					tf.data.Dataset.from_generator(
                       __lowerCamelCase			,     ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)			,     (
                           {'''input_ids''': tf.TensorShape([None]      ), '''attention_mask''': tf.TensorShape([None]      )},
                           tf.TensorShape([None]      ),
                       )			,     )
              else:
                   SCREAMING_SNAKE_CASE__					=					tf.data.Dataset.from_generator(
                       __lowerCamelCase			,     ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)			,     (
                           {
                               '''input_ids''': tf.TensorShape([None]      ),
                               '''attention_mask''': tf.TensorShape([None]      ),
                               '''token_type_ids''': tf.TensorShape([None]      ),
                           },
                           tf.TensorShape([None]      ),
                       )			,     )






         def    lowercase_	(							self		:	Optional[int]      )	->							int:
              SCREAMING_SNAKE_CASE__					=					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

              return self.dataset






         def __len__(							self		:	Tuple      )	->							Optional[int]:
              return len(self.features      )







         def __getitem__(							self		:	List[str]			,     __lowerCamelCase		:	Tuple      )	->							InputFeatures:
              return self.features[i]





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()


