





'''simple docstring'''




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

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


if is_sentencepiece_available():
				from .tokenization_pegasus import PegasusTokenizer
else:
				_A:					List[str]							=				None


_A:					Dict							=				logging.get_logger(__name__)


_A:					str							=				"""▁"""

_A:					Optional[Any]							=				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A:					Union[str, Any]							=				{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_A:					List[str]							=				{
    """google/pegasus-xsum""": 512,
}






class UpperCAmelCase							(		UpperCAmelCase_							):
				_A :			Tuple						   =       VOCAB_FILES_NAMES
				_A :			Optional[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
				_A :			Optional[int]						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_A :			Optional[int]						   =       PegasusTokenizer
				_A :			List[Any]						   =       ["""input_ids""", """attention_mask"""]



				def __init__(  self     ,			__A=None     ,			__A=None     ,			__A="<pad>"     ,			__A="</s>"     ,			__A="<unk>"     ,			__A="<mask_2>"     ,			__A="<mask_1>"     ,			__A=None     ,			__A=103     ,			**__A     ,			):
					__UpperCAmelCase					=   offset

					if additional_special_tokens is not None:
						if not isinstance(__A     ,			__A					):
							raise TypeError(
							    f'additional_special_tokens should be of type {type(__A					)}, but is'
							    f' {type(__A					)}'					)

						__UpperCAmelCase					=   (
						    ([mask_token_sent] + additional_special_tokens)
						    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
						    else additional_special_tokens
						)
						# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
						additional_special_tokens_extended += [
						    f'<unk_{i}>' for i in range(len(__A					)     ,			self.offset - 1					)
						]

						if len(set(__A					)					) != len(__A					):
							raise ValueError(
							    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
							    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
						__UpperCAmelCase					=   additional_special_tokens_extended
					else:
						__UpperCAmelCase					=   [mask_token_sent] if mask_token_sent is not None else []
						additional_special_tokens += [f'<unk_{i}>' for i in range(2     ,			self.offset					)]

					super().__init__(
					    __A     ,			tokenizer_file=__A     ,			pad_token=__A     ,			eos_token=__A     ,			unk_token=__A     ,			mask_token=__A     ,			mask_token_sent=__A     ,			offset=__A     ,			additional_special_tokens=__A     ,			**__A     ,			)
					__UpperCAmelCase					=   vocab_file
					__UpperCAmelCase					=   False if not self.vocab_file else True



				def 					__lowerCamelCase						(  self     ,			__A					):
					__UpperCAmelCase					=   set(self.all_special_ids					)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens					) + 3					)					):
						raise ValueError(
						    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
						    f' {len(self.additional_special_tokens					)} additional_special_tokens, but got {all_special_ids}'					)

					return [1 if x in all_special_ids else 0 for x in seq]



				def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = False					):
					if already_has_special_tokens:
						return self._special_token_mask(__A					)
					elif token_ids_a is None:
						return self._special_token_mask(__A					) + [1]
					else:
						return self._special_token_mask(token_ids_a + token_ids_a					) + [1]



				def 					__lowerCamelCase						(  self     ,			__A     ,			__A=None					):
					if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]




				def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.'					)

					if not os.path.isdir(__A					):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
						return
					__UpperCAmelCase					=   os.path.join(
					    __A     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

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

					return (out_vocab_file,)












'''simple docstring'''




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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=[]    )->     Any:
 __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_55
 __UpperCAmelCase					=   np.pad(_lowerCAmelCase       ,    mode='linear_ramp'       ,    pad_width=_lowerCAmelCase       ,    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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
 return max(_lowerCAmelCase       ,    min(_lowerCAmelCase       ,    _lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 __UpperCAmelCase					=   clamp_rect(_lowerCAmelCase       ,    [0, 0]       ,    [image_size[0], image_size[1]]    )
 return rect







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Tuple:
 __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(_lowerCAmelCase       ,    (original_slice, 0)    )
 return result







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					=   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 __UpperCAmelCase					=   tile.crop(_lowerCAmelCase    )
 return tile







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __UpperCAmelCase					=   n % d
 return n - divisor






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A = 350     ,			):
     super().__init__(
         vae=__A     ,			text_encoder=__A     ,			tokenizer=__A     ,			unet=__A     ,			low_res_scheduler=__A     ,			scheduler=__A     ,			max_noise_level=__A     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			**__A					):
     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(__A     ,			__A     ,			image.size					)
     __UpperCAmelCase					=   image.crop(__A					)
     __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     ,			__A					)
     __UpperCAmelCase					=   squeeze_tile(__A     ,			__A     ,			__A     ,			__A					)
     __UpperCAmelCase					=   to_input.size
     __UpperCAmelCase					=   to_input.resize((tile_size, tile_size)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   super(__A     ,			self					).__call__(image=__A     ,			**__A					).images[0]
     __UpperCAmelCase					=   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,			Image.BICUBIC					)
     __UpperCAmelCase					=   unsqueeze_tile(__A     ,			__A					)
     __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=__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     ,			):
     __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(__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 _lowerCAmelCase     (      )->     str:
 # Run a demo
 __UpperCAmelCase					=   'stabilityai/stable-diffusion-x4-upscaler'
 __UpperCAmelCase					=   StableDiffusionTiledUpscalePipeline.from_pretrained(_lowerCAmelCase       ,    revision='fp16'       ,    torch_dtype=torch.floataa    )
 __UpperCAmelCase					=   pipe.to('cuda'    )
 __UpperCAmelCase					=   Image.open('../../docs/source/imgs/diffusers_library.jpg'    )

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

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


if __name__ == "__main__":
    main()





