


"""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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=[]			)      ->			List[Any]:
							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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							return max(_SCREAMING_SNAKE_CASE			,	min(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Union[str, 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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							snake_case_          =						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
							snake_case_          =						tile.crop(_SCREAMING_SNAKE_CASE			)
							return tile







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						n % d
							return n - divisor





class        __A       (snake_case__):





							'''simple docstring'''

							def __init__(			self							: Tuple						,    UpperCAmelCase_							: AutoencoderKL						,    UpperCAmelCase_							: CLIPTextModel						,    UpperCAmelCase_							: CLIPTokenizer						,    UpperCAmelCase_							: UNetaDConditionModel						,    UpperCAmelCase_							: DDPMScheduler						,    UpperCAmelCase_							: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,    UpperCAmelCase_							: int = 350						,    )   ->str:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[Any]						,    UpperCAmelCase_							: Any						,    **UpperCAmelCase_							: str    )   ->List[str]:



														"""simple docstring"""



														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							: Optional[Any]						,    UpperCAmelCase_							: Union[str, List[str]]						,    UpperCAmelCase_							: Union[PIL.Image.Image, List[PIL.Image.Image]]						,    UpperCAmelCase_							: int = 75						,    UpperCAmelCase_							: float = 9.0						,    UpperCAmelCase_							: int = 50						,    UpperCAmelCase_							: Optional[Union[str, List[str]]] = None						,    UpperCAmelCase_							: Optional[int] = 1						,    UpperCAmelCase_							: float = 0.0						,    UpperCAmelCase_							: Optional[torch.Generator] = None						,    UpperCAmelCase_							: Optional[torch.FloatTensor] = None						,    UpperCAmelCase_							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,    UpperCAmelCase_							: int = 1						,    UpperCAmelCase_							: int = 128						,    UpperCAmelCase_							: int = 32						,    UpperCAmelCase_							: int = 32						,    )   ->str:



														"""simple docstring"""



														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   _a			( )      ->			Optional[Any]:
							# 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



