





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__ (					lowercase__  ,    lowercase__  ,    lowercase__=[]  ):






   '''simple docstring'''






   UpperCAmelCase_											=size[0] - overlap_pixels * 2
   UpperCAmelCase_											=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
   UpperCAmelCase_											=np.ones((size_y, size_x)  ,    dtype=np.uinta  ) * 2_5_5
   UpperCAmelCase_											=np.pad(lowercase__  ,    mode="linear_ramp"  ,    pad_width=lowercase__  ,    end_values=0  )

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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return max(lowercase__  ,    min(lowercase__  ,    lowercase__  )  )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=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(lowercase__  ,    (original_slice, 0)  )
   return result




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=n % d
   return n - divisor

class      A       (	__lowercase						):



      def __init__(	self:						Tuple		,  _lowerCAmelCase:						AutoencoderKL		,  _lowerCAmelCase:						CLIPTextModel		,  _lowerCAmelCase:						CLIPTokenizer		,  _lowerCAmelCase:						UNetaDConditionModel		,  _lowerCAmelCase:						DDPMScheduler		,  _lowerCAmelCase:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,  _lowerCAmelCase:						int = 350		,  )						->    Union[str, Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]		,  **_lowerCAmelCase:						Optional[Any]       )						->    str:
         '''simple docstring'''

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

         UpperCAmelCase_											=Image.new("RGB"		,  (image.size[0] * 4, image.size[1] * 4)       )
         UpperCAmelCase_											=math.ceil(image.size[0] / tile_size       )
         UpperCAmelCase_											=math.ceil(image.size[1] / tile_size       )
         UpperCAmelCase_											=tcx * tcy
         UpperCAmelCase_											=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     a__ (					):






   '''simple docstring'''






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

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

   UpperCAmelCase_											=pipe(image=lowercase__  ,    prompt="Black font, white background, vector"  ,    noise_level=4_0  ,    callback=lowercase__  )
   final_image.save("diffusers_library.jpg"  )


if __name__ == "__main__":
     main()











from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml

class      A       (	yaml.SafeLoader						):



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase_											=[tuple(_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else key for key in keys]
         UpperCAmelCase_											=Counter(_lowerCAmelCase       )
         UpperCAmelCase_											=[key for key in counter if counter[key] > 1]
         if duplicate_keys:
            raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'       )






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]=False       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=super().construct_mapping(_lowerCAmelCase		,  deep=_lowerCAmelCase       )
         self._check_no_duplicates_on_constructed_node(_lowerCAmelCase       )
         return mapping




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=list(readme_content.splitlines()  )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      UpperCAmelCase_											=full_content[1:].index("---"  ) + 1
      UpperCAmelCase_											="\n".join(full_content[1:sep_idx]  )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

   return None, "\n".join(lowercase__  )

class      A       (	__lowercase						):
      # class attributes
      _snake_case							     ={'''train_eval_index'''}  # train-eval-index in the YAML metadata



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Any		,  _lowerCAmelCase:						Path       )						->    "DatasetMetadata":
         '''simple docstring'''

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(readme_file.read()       )
         if yaml_string is not None:
            return cls.from_yaml_string(_lowerCAmelCase       )
         else:
            return cls()



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Path       )						->    List[str]:
         '''simple docstring'''

         if path.exists():
            with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
               UpperCAmelCase_											=readme_file.read()
         else:
            UpperCAmelCase_											=None
         UpperCAmelCase_											=self._to_readme(_lowerCAmelCase       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as readme_file:
            readme_file.write(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    str:
         '''simple docstring'''

         if readme_content is not None:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(_lowerCAmelCase       )
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n" + content
         else:
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n"
         return full_content



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[Any]		,  _lowerCAmelCase:						str       )						->    "DatasetMetadata":
         '''simple docstring'''

         UpperCAmelCase_											=yaml.load(_lowerCAmelCase		,  Loader=_NoDuplicateSafeLoader       ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase_											={
             (key.replace("-"		,  "_"       ) if key.replace("-"		,  "_"       ) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**_lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         return yaml.safe_dump(
             {
                 (key.replace("_"		,  "-"       ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }		,  sort_keys=_lowerCAmelCase		,  allow_unicode=_lowerCAmelCase		,  encoding="utf-8"		,  ).decode("utf-8"       )

__lowercase  :   int						      ={
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
     from argparse import ArgumentParser

     __lowercase  :   List[Any]						      =ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
     ap.add_argument("""readme_filepath""")
     __lowercase  :   int						      =ap.parse_args()

     __lowercase  :   Optional[int]						      =Path(args.readme_filepath)
     __lowercase  :   Tuple						      =DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)




