
'''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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=[]				):
       _snake_case       =  size[0] - overlap_pixels * 2
       _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
       _snake_case       =  np.ones((size_y, size_x)							, dtype=np.uinta				) * 255
       _snake_case       =  np.pad(_SCREAMING_SNAKE_CASE							, mode="""linear_ramp"""							, pad_width=_SCREAMING_SNAKE_CASE							, end_values=0				)

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       return max(_SCREAMING_SNAKE_CASE							, min(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  list(_SCREAMING_SNAKE_CASE				)
       rect[0] -= overlap
       rect[1] -= overlap
       rect[2] += overlap
       rect[3] += overlap
       _snake_case       =  clamp_rect(_SCREAMING_SNAKE_CASE							, [0, 0]							, [image_size[0], image_size[1]]				)
       return rect






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _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(_SCREAMING_SNAKE_CASE							, (original_slice, 0)				)
       return result






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
       _snake_case       =  tile.crop(_SCREAMING_SNAKE_CASE				)
       return tile






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  n % d
       return n - divisor


class       _lowerCAmelCase   (   __snake_case				):







       '''simple docstring'''

       def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 350  ,  )					->       Tuple:
              super().__init__(
                  vae=UpperCAmelCase  ,  text_encoder=UpperCAmelCase  ,  tokenizer=UpperCAmelCase  ,  unet=UpperCAmelCase  ,  low_res_scheduler=UpperCAmelCase  ,  scheduler=UpperCAmelCase  ,  max_noise_level=UpperCAmelCase  ,  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       List[str]:
              torch.manual_seed(0  )
              _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  ),
              )
              _snake_case       =  add_overlap_rect(UpperCAmelCase  ,  UpperCAmelCase  ,  image.size  )
              _snake_case       =  image.crop(UpperCAmelCase  )
              _snake_case       =  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
              _snake_case       =  translated_slice_x - (original_image_slice / 2)
              _snake_case       =  max(0  ,  UpperCAmelCase  )
              _snake_case       =  squeeze_tile(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )
              _snake_case       =  to_input.size
              _snake_case       =  to_input.resize((tile_size, tile_size)  ,  Image.BICUBIC  )
              _snake_case       =  super(UpperCAmelCase  ,  self  ).__call__(image=UpperCAmelCase  ,  **UpperCAmelCase  ).images[0]
              _snake_case       =  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)  ,  Image.BICUBIC  )
              _snake_case       =  unsqueeze_tile(UpperCAmelCase  ,  UpperCAmelCase  )
              _snake_case       =  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)  ,  Image.BICUBIC  )
              _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"""  )
              _snake_case       =  Image.fromarray(
                  make_transparency_mask(
                      (upscaled_tile.size[0], upscaled_tile.size[1])  ,  tile_border * 4  ,  remove_borders=UpperCAmelCase  )  ,  mode="""L"""  ,  )
              final_image.paste(
                  UpperCAmelCase  ,  (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)  ,  UpperCAmelCase  )





       @torch.no_grad()
       def __call__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 75  ,  UpperCAmelCase = 9.0  ,  UpperCAmelCase = 50  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 1  ,  UpperCAmelCase = 0.0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 1  ,  UpperCAmelCase = 128  ,  UpperCAmelCase = 32  ,  UpperCAmelCase = 32  ,  )					->       Tuple:
              _snake_case       =  Image.new("""RGB"""  ,  (image.size[0] * 4, image.size[1] * 4)  )
              _snake_case       =  math.ceil(image.size[0] / tile_size  )
              _snake_case       =  math.ceil(image.size[1] / tile_size  )
              _snake_case       =  tcx * tcy
              _snake_case       =  0
              for y in range(UpperCAmelCase  ):
                     for x in range(UpperCAmelCase  ):
                            self._process_tile(
                                UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  prompt=UpperCAmelCase  ,  num_inference_steps=UpperCAmelCase  ,  guidance_scale=UpperCAmelCase  ,  noise_level=UpperCAmelCase  ,  negative_prompt=UpperCAmelCase  ,  num_images_per_prompt=UpperCAmelCase  ,  eta=UpperCAmelCase  ,  generator=UpperCAmelCase  ,  latents=UpperCAmelCase  ,  )
                            current_count += 1
                            if callback is not None:
                                   callback({"""progress""": current_count / total_tile_count, """image""": final_image}  )
              return final_image






def  __SCREAMING_SNAKE_CASE				(  ):
       # Run a demo
       _snake_case       =  """stabilityai/stable-diffusion-x4-upscaler"""
       _snake_case       =  StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE							, revision="""fp16"""							, torch_dtype=torch.floataa				)
       _snake_case       =  pipe.to("""cuda"""				)
       _snake_case       =  Image.open("""../../docs/source/imgs/diffusers_library.jpg"""				)

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

       _snake_case       =  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()

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)