


'''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							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   int=[]   ):
					__a			:       Optional[Any]					    =  size[0] - overlap_pixels * 2
					__a			:       int					    =  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			:       Tuple					    =  np.ones((size_y, size_x)						,      dtype=np.uinta   ) * 255
					__a			:       List[Any]					    =  np.pad(SCREAMING_SNAKE_CASE_						,      mode='linear_ramp'						,      pad_width=SCREAMING_SNAKE_CASE_						,      end_values=0   )

					if "l" in remove_borders:
										__a			:       Optional[Any]					    =  mask[:, overlap_pixels : mask.shape[1]]
					if "r" in remove_borders:
										__a			:       List[Any]					    =  mask[:, 0 : mask.shape[1] - overlap_pixels]
					if "t" in remove_borders:
										__a			:       List[Any]					    =  mask[overlap_pixels : mask.shape[0], :]
					if "b" in remove_borders:
										__a			:       Tuple					    =  mask[0 : mask.shape[0] - overlap_pixels, :]
					return mask






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Tuple   ):
					return max(SCREAMING_SNAKE_CASE_						,      min(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					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							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       Optional[Any]					    =  list(SCREAMING_SNAKE_CASE_   )
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					__a			:       Any					    =  clamp_rect(SCREAMING_SNAKE_CASE_						,      [0, 0]						,      [image_size[0], image_size[1]]   )
					return rect






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Dict					    =  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(SCREAMING_SNAKE_CASE_						,      (original_slice, 0)   )
					return result






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Union[str, Any]					    =  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					__a			:       int					    =  tile.crop(SCREAMING_SNAKE_CASE_   )
					return tile






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  n % d
					return n - divisor





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a = 350						,				):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				**__a							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       Tuple					    =  (
						    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			:       Union[str, Any]					    =  add_overlap_rect(__A						,				__A						,				image.size							)
						__a			:       List[str]					    =  image.crop(__A							)
						__a			:       Optional[int]					    =  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
						__a			:       int					    =  translated_slice_x - (original_image_slice / 2)
						__a			:       Optional[Any]					    =  max(0						,				__A							)
						__a			:       Tuple					    =  squeeze_tile(__A						,				__A						,				__A						,				__A							)
						__a			:       Dict					    =  to_input.size
						__a			:       Optional[int]					    =  to_input.resize((tile_size, tile_size)						,				Image.BICUBIC							)
						__a			:       Dict					    =  super(__A						,				self							).__call__(image=__A						,				**__A							).images[0]
						__a			:       Tuple					    =  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC							)
						__a			:       Optional[Any]					    =  unsqueeze_tile(__A						,				__A							)
						__a			:       Optional[Any]					    =  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC							)
						__a			:       int					    =  []
						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			:       int					    =  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						,				):

						'''simple docstring'''





						__a			:       Dict					    =  Image.new('RGB'						,				(image.size[0] * 4, image.size[1] * 4)							)
						__a			:       Union[str, Any]					    =  math.ceil(image.size[0] / tile_size							)
						__a			:       Dict					    =  math.ceil(image.size[1] / tile_size							)
						__a			:       str					    =  tcx * tcy
						__a			:       int					    =  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							():
					__a			:       Dict					    =  "stabilityai/stable-diffusion-x4-upscaler"
					__a			:       Union[str, Any]					    =  StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_						,      revision='fp16'						,      torch_dtype=torch.floataa   )
					__a			:       Optional[Any]					    =  pipe.to('cuda'   )
					__a			:       List[str]					    =  Image.open('../../docs/source/imgs/diffusers_library.jpg'   )

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

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


if __name__ == "__main__":
	main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



