

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						Optional[int]         =    {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    ['''BlenderbotTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						str         =    [
           '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''BlenderbotForCausalLM''',
           '''BlenderbotForConditionalGeneration''',
           '''BlenderbotModel''',
           '''BlenderbotPreTrainedModel''',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Any         =    [
           '''TFBlenderbotForConditionalGeneration''',
           '''TFBlenderbotModel''',
           '''TFBlenderbotPreTrainedModel''',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Dict         =    [
           '''FlaxBlenderbotForConditionalGeneration''',
           '''FlaxBlenderbotModel''',
           '''FlaxBlenderbotPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						Tuple         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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        UpperCAmelCase_     (		_A   ,	_A   ,	_A=[]       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					size[0] - overlap_pixels * 2
     SCREAMING_SNAKE_CASE__					=					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
     SCREAMING_SNAKE_CASE__					=					np.ones((size_y, size_x)   ,	dtype=np.uinta       ) * 2_55
     SCREAMING_SNAKE_CASE__					=					np.pad(_A   ,	mode='''linear_ramp'''   ,	pad_width=_A   ,	end_values=0       )

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





     '''simple docstring'''




     return max(_A   ,	min(_A   ,	_A       )       )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''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        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					list(_A       )
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     SCREAMING_SNAKE_CASE__					=					clamp_rect(_A   ,	[0, 0]   ,	[image_size[0], image_size[1]]       )
     return rect
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     SCREAMING_SNAKE_CASE__					=					tile.crop(_A       )
     return tile
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					n % d
     return n - divisor






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	int			,     __lowerCamelCase		:	AutoencoderKL			,     __lowerCamelCase		:	CLIPTextModel			,     __lowerCamelCase		:	CLIPTokenizer			,     __lowerCamelCase		:	UNetaDConditionModel			,     __lowerCamelCase		:	DDPMScheduler			,     __lowerCamelCase		:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,     __lowerCamelCase		:	int = 350			,     )	->							int:
       super().__init__(
           vae=__lowerCamelCase			,     text_encoder=__lowerCamelCase			,     tokenizer=__lowerCamelCase			,     unet=__lowerCamelCase			,     low_res_scheduler=__lowerCamelCase			,     scheduler=__lowerCamelCase			,     max_noise_level=__lowerCamelCase			,     )






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     **__lowerCamelCase		:	Tuple      )	->							Dict:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					(
           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      ),
       )
       SCREAMING_SNAKE_CASE__					=					add_overlap_rect(__lowerCamelCase			,     __lowerCamelCase			,     image.size      )
       SCREAMING_SNAKE_CASE__					=					image.crop(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       SCREAMING_SNAKE_CASE__					=					translated_slice_x - (original_image_slice / 2)
       SCREAMING_SNAKE_CASE__					=					max(0			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					squeeze_tile(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					to_input.size
       SCREAMING_SNAKE_CASE__					=					to_input.resize((tile_size, tile_size)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					super(__lowerCamelCase			,     self      ).__call__(image=__lowerCamelCase			,     **__lowerCamelCase      ).images[0]
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					unsqueeze_tile(__lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					[]
       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'''      )
       SCREAMING_SNAKE_CASE__					=					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		:	List[str]			,     __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			,     )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					Image.new('''RGB'''			,     (image.size[0] * 4, image.size[1] * 4)      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[0] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[1] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					tcx * tcy
       SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		):





     '''simple docstring'''




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

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

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


if __name__ == "__main__":
       main()


