






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 						_a    (      a :Optional[Any]	,							a :Optional[Any]	,							a :Any=[] )							->			Optional[int]:
 a		  =      size[0] - overlap_pixels * 2
 a		  =      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
 a		  =      np.ones((size_y, size_x)	,							dtype=np.uinta ) * 255
 a		  =      np.pad(a	,							mode='''linear_ramp'''	,							pad_width=a	,							end_values=0 )

 if "l" in remove_borders:
  a		  =      mask[:, overlap_pixels : mask.shape[1]]
 if "r" in remove_borders:
  a		  =      mask[:, 0 : mask.shape[1] - overlap_pixels]
 if "t" in remove_borders:
  a		  =      mask[overlap_pixels : mask.shape[0], :]
 if "b" in remove_borders:
  a		  =      mask[0 : mask.shape[0] - overlap_pixels, :]
 return mask
def 						_a    (      a :Optional[Any]	,							a :Optional[int]	,							a :Union[str, Any] )							->			Any:
 return max(a	,							min(a	,							a ) )
def 						_a    (      a :[int]	,							a :[int]	,							a :[int] )							->			int:
 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 						_a    (      a :[int]	,							a :int	,							a :[int] )							->			Optional[int]:
 a		  =      list(a )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 a		  =      clamp_rect(a	,							[0, 0]	,							[image_size[0], image_size[1]] )
 return rect
def 						_a    (      a :List[str]	,							a :str	,							a :Optional[int]	,							a :List[Any] )							->			Dict:
 a		  =      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 						_a    (      a :Union[str, Any]	,							a :List[Any] )							->			Dict:
 a		  =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 a		  =      tile.crop(a )
 return tile
def 						_a    (      a :List[str]	,							a :Optional[Any] )							->			int:
 a		  =      n % d
 return n - divisor






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Any ,					__UpperCAmelCase      :						AutoencoderKL ,					__UpperCAmelCase      :						CLIPTextModel ,					__UpperCAmelCase      :						CLIPTokenizer ,					__UpperCAmelCase      :						UNetaDConditionModel ,					__UpperCAmelCase      :						DDPMScheduler ,					__UpperCAmelCase      :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,					__UpperCAmelCase      :						int = 350 ,					)    ->int:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					**__UpperCAmelCase      :						Dict  )    ->Optional[Any]:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      (
           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  ),
       )
       a		  =      add_overlap_rect(__UpperCAmelCase ,					__UpperCAmelCase ,					image.size  )
       a		  =      image.crop(__UpperCAmelCase  )
       a		  =      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       a		  =      translated_slice_x - (original_image_slice / 2)
       a		  =      max(0 ,					__UpperCAmelCase  )
       a		  =      squeeze_tile(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      to_input.size
       a		  =      to_input.resize((tile_size, tile_size) ,					Image.BICUBIC  )
       a		  =      super(__UpperCAmelCase ,					self  ).__call__(image=__UpperCAmelCase ,					**__UpperCAmelCase  ).images[0]
       a		  =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,					Image.BICUBIC  )
       a		  =      unsqueeze_tile(__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,					Image.BICUBIC  )
       a		  =      []
       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'''  )
       a		  =      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      :						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 = 128 ,					__UpperCAmelCase      :						int = 32 ,					__UpperCAmelCase      :						int = 32 ,					)    ->Optional[int]:




       """simple docstring"""


       a		  =      Image.new('''RGB''' ,					(image.size[0] * 4, image.size[1] * 4)  )
       a		  =      math.ceil(image.size[0] / tile_size  )
       a		  =      math.ceil(image.size[1] / tile_size  )
       a		  =      tcx * tcy
       a		  =      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 						_a    (      )							->			Dict:
 # Run a demo
 a		  =      '''stabilityai/stable-diffusion-x4-upscaler'''
 a		  =      StableDiffusionTiledUpscalePipeline.from_pretrained(a	,							revision='''fp16'''	,							torch_dtype=torch.floataa )
 a		  =      pipe.to('''cuda''' )
 a		  =      Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )

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

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


if __name__ == "__main__":
     main()










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCAmelCase__				   =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				   =		{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": 512,
    "google/electra-base-generator": 512,
    "google/electra-large-generator": 512,
    "google/electra-small-discriminator": 512,
    "google/electra-base-discriminator": 512,
    "google/electra-large-discriminator": 512,
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  VOCAB_FILES_NAMES
      __snake_case       =  PRETRAINED_VOCAB_FILES_MAP
      __snake_case       =  PRETRAINED_INIT_CONFIGURATION
      __snake_case       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case       =  ElectraTokenizer







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						str="[UNK]" ,					__UpperCAmelCase      :						Any="[SEP]" ,					__UpperCAmelCase      :						str="[PAD]" ,					__UpperCAmelCase      :						Optional[Any]="[CLS]" ,					__UpperCAmelCase      :						Union[str, Any]="[MASK]" ,					__UpperCAmelCase      :						List[str]=True ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->str:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase ,					tokenizer_file=__UpperCAmelCase ,					do_lower_case=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					tokenize_chinese_chars=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

       a		  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get('''lowercase''' ,					__UpperCAmelCase  ) != do_lower_case
           or normalizer_state.get('''strip_accents''' ,					__UpperCAmelCase  ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars''' ,					__UpperCAmelCase  ) != tokenize_chinese_chars
       ):
        a		  =      getattr(__UpperCAmelCase ,					normalizer_state.pop('''type'''  )  )
        a		  =      do_lower_case
        a		  =      strip_accents
        a		  =      tokenize_chinese_chars
        a		  =      normalizer_class(**__UpperCAmelCase  )

       a		  =      do_lower_case







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Tuple=None  )    ->str:




       """simple docstring"""


       a		  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[int] ,					__UpperCAmelCase      :						Optional[List[int]] = None  )    ->List[int]:




       """simple docstring"""


       a		  =      [self.sep_token_id]
       a		  =      [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      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       a		  =      self._tokenizer.model.save(__UpperCAmelCase ,					name=__UpperCAmelCase  )
       return tuple(__UpperCAmelCase  )


