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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       Union[str, Any]=[]	)							->					Any:







  """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(snake_case__ ,     mode="""linear_ramp""" ,     pad_width=snake_case__ ,     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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Any	)							->					Union[str, Any]:







  """simple docstring"""





  return max(snake_case__ ,     min(snake_case__ ,     snake_case__	)	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Tuple	)							->					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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       List[Any]	)							->					Dict:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       int	)							->					Optional[Any]:







  """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(snake_case__ ,     (original_slice, 0)	)
  return result



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       List[str]	)							->					str:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Dict	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      n % d
  return n - divisor







class __snake_case				(						__snake_case		):




   def __init__(    self			:				Optional[int]       ,	_lowercase			:				int       ,	_lowercase			:				Tuple       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				List[str]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Optional[Any] = 3_50       ,	):



     """simple docstring"""






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




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				str       ,	_lowercase			:				int       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[int]       ,	**_lowercase			:				Dict      ):



     """simple docstring"""






     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(_lowercase       ,	_lowercase       ,	image.size      )
     SCREAMING_SNAKE_CASE__										=      image.crop(_lowercase      )
     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       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      squeeze_tile(_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      to_input.size
     SCREAMING_SNAKE_CASE__										=      to_input.resize((tile_size, tile_size)       ,	Image.BICUBIC      )
     SCREAMING_SNAKE_CASE__										=      super(_lowercase       ,	self      ).__call__(image=_lowercase       ,	**_lowercase      ).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(_lowercase       ,	_lowercase      )
     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=_lowercase      )       ,	mode="""L"""       ,	)
     final_image.paste(
         _lowercase       ,	(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)       ,	_lowercase      )




   @torch.no_grad()
   def __call__(    self			:				int       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[str] = 75       ,	_lowercase			:				Optional[int] = 9.0       ,	_lowercase			:				int = 50       ,	_lowercase			:				str = None       ,	_lowercase			:				int = 1       ,	_lowercase			:				str = 0.0       ,	_lowercase			:				str = None       ,	_lowercase			:				Union[str, Any] = None       ,	_lowercase			:				List[str] = None       ,	_lowercase			:				Tuple = 1       ,	_lowercase			:				Union[str, Any] = 1_28       ,	_lowercase			:				Optional[int] = 32       ,	_lowercase			:				Optional[Any] = 32       ,	):



     """simple docstring"""






     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(_lowercase      ):
       for x in range(_lowercase      ):
         self._process_tile(
             _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase       ,	prompt=_lowercase       ,	num_inference_steps=_lowercase       ,	guidance_scale=_lowercase       ,	noise_level=_lowercase       ,	negative_prompt=_lowercase       ,	num_images_per_prompt=_lowercase       ,	eta=_lowercase       ,	generator=_lowercase       ,	latents=_lowercase       ,	)
         current_count += 1
         if callback is not None:
           callback({"""progress""": current_count / total_tile_count, """image""": final_image}      )
     return final_image





def 	__SCREAMING_SNAKE_CASE  (					)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      '''stabilityai/stable-diffusion-x4-upscaler'''
  SCREAMING_SNAKE_CASE__										=      StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case__ ,     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(__UpperCamelCase    :       Union[str, Any]	):
    print(f"""progress: {obj['progress']:.4f}"""	)
    obj["image"].save("""diffusers_library_progress.jpg"""	)

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


if __name__ == "__main__":
   main()


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         super().__init__(
             SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			do_lower_case=SCREAMING_SNAKE_CASE						,			remove_space=SCREAMING_SNAKE_CASE						,			keep_accents=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
             return
         A       :   Any					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)




