





'''simple docstring'''




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
				import torch






class UpperCAmelCase							(		UpperCamelCase_							):
				_A :			Union[str, Any]						   =       """char"""
				_A :			List[Any]						   =       """bpe"""
				_A :			Tuple						   =       """wp"""



_A:					List[Any]							=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class UpperCAmelCase							(		UpperCamelCase_							):
				_A :			Tuple						   =       ["""image_processor""", """char_tokenizer"""]
				_A :			List[Any]						   =       """ViTImageProcessor"""
				_A :			Any						   =       """MgpstrTokenizer"""



				def __init__(  self     ,			__A=None     ,			__A=None     ,			**__A					):
					__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.'     ,			__a     ,			)
						__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`.'					)

					__UpperCAmelCase					=   tokenizer
					__UpperCAmelCase					=   AutoTokenizer.from_pretrained('gpt2'					)
					__UpperCAmelCase					=   AutoTokenizer.from_pretrained('bert-base-uncased'					)

					super().__init__(__a     ,			__a					)



				def __call__(  self     ,			__A=None     ,			__A=None     ,			__A=None     ,			**__A					):






					if images is None and text is None:
						raise ValueError('You need to specify either an `images` or `text` input to process.'					)

					if images is not None:
						__UpperCAmelCase					=   self.image_processor(__a     ,			return_tensors=__a     ,			**__a					)
					if text is not None:
						__UpperCAmelCase					=   self.char_tokenizer(__a     ,			return_tensors=__a     ,			**__a					)

					if text is None:
						return inputs
					elif images is None:
						return encodings
					else:
						__UpperCAmelCase					=   encodings['input_ids']
						return inputs



				def 					__lowerCamelCase						(  self     ,			__A					):
					__UpperCAmelCase					=   sequences
					__UpperCAmelCase					=   char_preds.size(0					)

					__UpperCAmelCase					=   self._decode_helper(__a     ,			'char'					)
					__UpperCAmelCase					=   self._decode_helper(__a     ,			'bpe'					)
					__UpperCAmelCase					=   self._decode_helper(__a     ,			'wp'					)

					__UpperCAmelCase					=   []
					__UpperCAmelCase					=   []
					for i in range(__a					):
						__UpperCAmelCase					=   [char_scores[i], bpe_scores[i], wp_scores[i]]
						__UpperCAmelCase					=   [char_strs[i], bpe_strs[i], wp_strs[i]]
						__UpperCAmelCase					=   scores.index(max(__a					)					)
						final_strs.append(strs[max_score_index]					)
						final_scores.append(scores[max_score_index]					)

					__UpperCAmelCase					=   {}
					__UpperCAmelCase					=   final_strs
					__UpperCAmelCase					=   final_scores
					__UpperCAmelCase					=   char_strs
					__UpperCAmelCase					=   bpe_strs
					__UpperCAmelCase					=   wp_strs
					return out



				def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):






					if format == DecodeType.CHARACTER:
						__UpperCAmelCase					=   self.char_decode
						__UpperCAmelCase					=   1
						__UpperCAmelCase					=   '[s]'
					elif format == DecodeType.BPE:
						__UpperCAmelCase					=   self.bpe_decode
						__UpperCAmelCase					=   2
						__UpperCAmelCase					=   '#'
					elif format == DecodeType.WORDPIECE:
						__UpperCAmelCase					=   self.wp_decode
						__UpperCAmelCase					=   102
						__UpperCAmelCase					=   '[SEP]'
					else:
						raise ValueError(f'Format {format} is not supported.'					)

					__UpperCAmelCase					=   [], []
					__UpperCAmelCase					=   pred_logits.size(0					)
					__UpperCAmelCase					=   pred_logits.size(1					)
					__UpperCAmelCase					=   pred_logits.topk(1     ,			dim=-1     ,			largest=__a     ,			sorted=__a					)
					__UpperCAmelCase					=   preds_index.view(-1     ,			__a					)[:, 1:]
					__UpperCAmelCase					=   decoder(__a					)
					__UpperCAmelCase					=   torch.nn.functional.softmax(__a     ,			dim=2					).max(dim=2					)
					__UpperCAmelCase					=   preds_max_prob[:, 1:]

					for index in range(__a					):
						__UpperCAmelCase					=   preds_str[index].find(__a					)
						__UpperCAmelCase					=   preds_str[index][:pred_eos]
						__UpperCAmelCase					=   preds_index[index].cpu().tolist()
						__UpperCAmelCase					=   pred_index.index(__a					) if eos_token in pred_index else -1
						__UpperCAmelCase					=   preds_max_prob[index][: pred_eos_index + 1]
						__UpperCAmelCase					=   pred_max_prob.cumprod(dim=0					)[-1] if pred_max_prob.nelement() != 0 else 0.0
						dec_strs.append(__a					)
						conf_scores.append(__a					)

					return dec_strs, conf_scores



				def 					__lowerCamelCase						(  self     ,			__A					):
					__UpperCAmelCase					=   [seq.replace(' '     ,			''					) for seq in self.char_tokenizer.batch_decode(__a					)]
					return decode_strs



				def 					__lowerCamelCase						(  self     ,			__A					):
					return self.bpe_tokenizer.batch_decode(__a					)




				def 					__lowerCamelCase						(  self     ,			__A					):
					__UpperCAmelCase					=   [seq.replace(' '     ,			''					) for seq in self.wp_tokenizer.batch_decode(__a					)]
					return decode_strs












'''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()





