






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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Union[str, Any]=[]					):
							__lowercase					:							int			=   size[0] - overlap_pixels * 2
							__lowercase					:							Union[str, Any]			=   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
							__lowercase					:							List[str]			=   np.ones((size_y, size_x)				,				dtype=np.uinta					) * 255
							__lowercase					:							Tuple			=   np.pad(lowerCAmelCase_				,				mode="""linear_ramp"""				,				pad_width=lowerCAmelCase_				,				end_values=0					)

							if "l" in remove_borders:
														__lowercase					:							Optional[Any]			=   mask[:, overlap_pixels : mask.shape[1]]
							if "r" in remove_borders:
														__lowercase					:							Union[str, Any]			=   mask[:, 0 : mask.shape[1] - overlap_pixels]
							if "t" in remove_borders:
														__lowercase					:							Tuple			=   mask[overlap_pixels : mask.shape[0], :]
							if "b" in remove_borders:
														__lowercase					:							str			=   mask[0 : mask.shape[0] - overlap_pixels, :]
							return mask
def 					snake_case_						(   lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  List[str]				,				lowerCAmelCase_				:  Optional[Any]					):
							return max(lowerCAmelCase_				,				min(lowerCAmelCase_				,				lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  [int]				,				lowerCAmelCase_				:  [int]				,				lowerCAmelCase_				:  [int]					):
							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 					snake_case_						(   lowerCAmelCase_				:  [int]				,				lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  [int]					):
							__lowercase					:							List[Any]			=   list(lowerCAmelCase_					)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							__lowercase					:							Union[str, Any]			=   clamp_rect(lowerCAmelCase_				,				[0, 0]				,				[image_size[0], image_size[1]]					)
							return rect
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  List[str]				,				lowerCAmelCase_				:  str					):
							__lowercase					:							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(lowerCAmelCase_				,				(original_slice, 0)					)
							return result
def 					snake_case_						(   lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Union[str, Any]					):
							__lowercase					:							List[Any]			=   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							__lowercase					:							List[str]			=   tile.crop(lowerCAmelCase_					)
							return tile
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  str					):
							__lowercase					:							List[str]			=   n % d
							return n - divisor

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

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


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Optional[Any]						,   __a :		Any						,   __a :		Dict						,   __a :		Optional[Any]						,   __a :		str						,   __a :		Any						,   __a :		Any						,   **__a :		Dict							)	->     Tuple:


												"""simple docstring"""

												torch.manual_seed(0							)
												__lowercase					:							List[Any]			=   (
												    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							),
												)
												__lowercase					:							Any			=   add_overlap_rect(__a						,   __a						,   image.size							)
												__lowercase					:							Tuple			=   image.crop(__a							)
												__lowercase					:							Tuple			=   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
												__lowercase					:							str			=   translated_slice_x - (original_image_slice / 2)
												__lowercase					:							Optional[int]			=   max(0						,   __a							)
												__lowercase					:							int			=   squeeze_tile(__a						,   __a						,   __a						,   __a							)
												__lowercase					:							Optional[int]			=   to_input.size
												__lowercase					:							Union[str, Any]			=   to_input.resize((tile_size, tile_size)						,   Image.BICUBIC							)
												__lowercase					:							List[Any]			=   super(__a						,   self							).__call__(image=__a						,   **__a							).images[0]
												__lowercase					:							str			=   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,   Image.BICUBIC							)
												__lowercase					:							str			=   unsqueeze_tile(__a						,   __a							)
												__lowercase					:							Optional[int]			=   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,   Image.BICUBIC							)
												__lowercase					:							List[str]			=   []
												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"""							)
												__lowercase					:							Optional[Any]			=   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 :		str						,   __a :		Union[str, List[str]]						,   __a :		Union[PIL.Image.Image, List[PIL.Image.Image]]						,   __a :		int = 75						,   __a :		float = 9.0						,   __a :		int = 50						,   __a :		Optional[Union[str, List[str]]] = None						,   __a :		Optional[int] = 1						,   __a :		float = 0.0						,   __a :		Optional[torch.Generator] = None						,   __a :		Optional[torch.FloatTensor] = None						,   __a :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,   __a :		int = 1						,   __a :		int = 128						,   __a :		int = 32						,   __a :		int = 32						,   )	->     Any:


												"""simple docstring"""

												__lowercase					:							Tuple			=   Image.new("""RGB"""						,   (image.size[0] * 4, image.size[1] * 4)							)
												__lowercase					:							Any			=   math.ceil(image.size[0] / tile_size							)
												__lowercase					:							List[str]			=   math.ceil(image.size[1] / tile_size							)
												__lowercase					:							Tuple			=   tcx * tcy
												__lowercase					:							List[Any]			=   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 					snake_case_						(   ):
							# Run a demo
							__lowercase					:							Optional[Any]			=   """stabilityai/stable-diffusion-x4-upscaler"""
							__lowercase					:							Any			=   StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCAmelCase_				,				revision="""fp16"""				,				torch_dtype=torch.floataa					)
							__lowercase					:							List[Any]			=   pipe.to("""cuda"""					)
							__lowercase					:							Dict			=   Image.open("""../../docs/source/imgs/diffusers_library.jpg"""					)

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

							__lowercase					:							Any			=   pipe(image=lowerCAmelCase_				,				prompt="""Black font, white background, vector"""				,				noise_level=40				,				callback=lowerCAmelCase_					)
							final_image.save("""diffusers_library.jpg"""					)


if __name__ == "__main__":
	main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       List[str]        =				{
    '''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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

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

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride