



'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""image_processor""", """tokenizer"""]
    _SCREAMING_SNAKE_CASE	  =  """ViltImageProcessor"""
    _SCREAMING_SNAKE_CASE	  =  ("""BertTokenizer""", """BertTokenizerFast""")






    def __init__(  self      :					Any  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					List[Any]=None  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  None
       if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'  ,						UpperCamelCase__  ,						)
          UpperCamelCase					=  kwargs.pop('feature_extractor' )

       UpperCamelCase					=  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.' )
       if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.' )

       super().__init__(UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  self.image_processor






    def __call__(  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Union[bool, str, PaddingStrategy] = False  ,						UpperCamelCase__      :					Union[bool, str, TruncationStrategy] = None  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					int = 0  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[Union[str, TensorType]] = None  ,						**UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(
           text=UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						stride=UpperCamelCase__  ,						pad_to_multiple_of=UpperCamelCase__  ,						return_token_type_ids=UpperCamelCase__  ,						return_attention_mask=UpperCamelCase__  ,						return_overflowing_tokens=UpperCamelCase__  ,						return_special_tokens_mask=UpperCamelCase__  ,						return_offsets_mapping=UpperCamelCase__  ,						return_length=UpperCamelCase__  ,						verbose=UpperCamelCase__  ,						return_tensors=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
       # add pixel_values + pixel_mask
       UpperCamelCase					=  self.image_processor(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ )
       encoding.update(UpperCamelCase__ )

       return encoding






    def  A       (  self      :					int  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







       return self.tokenizer.batch_decode(*UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					Tuple  ,						*UpperCamelCase__      :					Optional[int]  ,						**UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       return self.tokenizer.decode(*UpperCamelCase__  ,						**UpperCamelCase__ )






    @property
    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer.model_input_names
       UpperCamelCase					=  self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






    @property
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,						UpperCamelCase__  ,						)
       return self.image_processor_class






    @property
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,						UpperCamelCase__  ,						)
       return self.image_processor





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_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=[]	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				size[0] - overlap_pixels * 2
  lowerCamelCase :					int    =				size[1] - overlap_pixels * 2
  for letter in ["l", "r"]:
    if letter in remove_borders:
      size_x += overlap_pixels
  for letter in ["t", "b"]:
    if letter in remove_borders:
      size_y += overlap_pixels
  lowerCamelCase :					Tuple    =				np.ones((size_y, size_x)      ,						dtype=np.uinta	) * 255
  lowerCamelCase :					List[Any]    =				np.pad(SCREAMING_SNAKE_CASE_      ,						mode="linear_ramp"      ,						pad_width=SCREAMING_SNAKE_CASE_      ,						end_values=0	)

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return max(SCREAMING_SNAKE_CASE_      ,						min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				list(SCREAMING_SNAKE_CASE_	)
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  lowerCamelCase :					Any    =				clamp_rect(SCREAMING_SNAKE_CASE_      ,						[0, 0]      ,						[image_size[0], image_size[1]]	)
  return rect






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					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(SCREAMING_SNAKE_CASE_      ,						(original_slice, 0)	)
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  lowerCamelCase :					int    =				tile.crop(SCREAMING_SNAKE_CASE_	)
  return tile






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				n % d
  return n - divisor
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A = 350						,				):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					Tuple    =				(
         min(image.size[0] - (tile_size + original_image_slice)						,				x * tile_size ),
         min(image.size[1] - (tile_size + original_image_slice)						,				y * tile_size ),
         min(image.size[0]						,				(x + 1) * tile_size ),
         min(image.size[1]						,				(y + 1) * tile_size ),
     )
     lowerCamelCase :					Union[str, Any]    =				add_overlap_rect(__A						,				__A						,				image.size )
     lowerCamelCase :					List[str]    =				image.crop(__A )
     lowerCamelCase :					Optional[int]    =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     lowerCamelCase :					int    =				translated_slice_x - (original_image_slice / 2)
     lowerCamelCase :					Optional[Any]    =				max(0						,				__A )
     lowerCamelCase :					Tuple    =				squeeze_tile(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					Dict    =				to_input.size
     lowerCamelCase :					Optional[int]    =				to_input.resize((tile_size, tile_size)						,				Image.BICUBIC )
     lowerCamelCase :					Dict    =				super(__A						,				self ).__call__(image=__A						,				**__A ).images[0]
     lowerCamelCase :					Tuple    =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					Optional[Any]    =				unsqueeze_tile(__A						,				__A )
     lowerCamelCase :					Optional[Any]    =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					int    =				[]
     if x == 0:
       remove_borders.append("l" )
     elif crop_rect[2] == image.size[0]:
       remove_borders.append("r" )
     if y == 0:
       remove_borders.append("t" )
     elif crop_rect[3] == image.size[1]:
       remove_borders.append("b" )
     lowerCamelCase :					int    =				Image.fromarray(
         make_transparency_mask(
             (upscaled_tile.size[0], upscaled_tile.size[1])						,				tile_border * 4						,				remove_borders=__A )						,				mode="L"						,				)
     final_image.paste(
         __A						,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)						,				__A )






   @torch.no_grad()
   def __call__(		self						,				__A						,				__A						,				__A = 75						,				__A = 9.0						,				__A = 50						,				__A = None						,				__A = 1						,				__A = 0.0						,				__A = None						,				__A = None						,				__A = None						,				__A = 1						,				__A = 128						,				__A = 32						,				__A = 32						,				):






     """simple docstring"""





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






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"stabilityai/stable-diffusion-x4-upscaler"
  lowerCamelCase :					Union[str, Any]    =				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,						revision="fp16"      ,						torch_dtype=torch.floataa	)
  lowerCamelCase :					Optional[Any]    =				pipe.to("cuda"	)
  lowerCamelCase :					List[str]    =				Image.open("../../docs/source/imgs/diffusers_library.jpg"	)

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

  lowerCamelCase :					int    =				pipe(image=SCREAMING_SNAKE_CASE_      ,						prompt="Black font, white background, vector"      ,						noise_level=40      ,						callback=SCREAMING_SNAKE_CASE_	)
  final_image.save("diffusers_library.jpg"	)


if __name__ == "__main__":
   main()



