





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__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  List[Any]=[]       )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						size[0] - overlap_pixels * 2
							SCREAMING_SNAKE_CASE__       :      Optional[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
							SCREAMING_SNAKE_CASE__       :      Tuple     =						np.ones((size_y, size_x)			,				dtype=np.uinta       ) * 2_55
							SCREAMING_SNAKE_CASE__       :      str     =						np.pad(SCREAMING_SNAKE_CASE__			,				mode="linear_ramp"			,				pad_width=SCREAMING_SNAKE_CASE__			,				end_values=0       )

							if "l" in remove_borders:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						mask[:, overlap_pixels : mask.shape[1]]
							if "r" in remove_borders:
														SCREAMING_SNAKE_CASE__       :      int     =						mask[:, 0 : mask.shape[1] - overlap_pixels]
							if "t" in remove_borders:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						mask[overlap_pixels : mask.shape[0], :]
							if "b" in remove_borders:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						mask[0 : mask.shape[0] - overlap_pixels, :]
							return mask




def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    List[Any]:

							'''simple docstring'''






							return max(SCREAMING_SNAKE_CASE__			,				min(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )




def _a (  SCREAMING_SNAKE_CASE__      :  [int]			,				SCREAMING_SNAKE_CASE__      :  [int]			,				SCREAMING_SNAKE_CASE__      :  [int]       )	->    Optional[int]:

							'''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 _a (  SCREAMING_SNAKE_CASE__      :  [int]			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  [int]       )	->    Any:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						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__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  int       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tile.crop(SCREAMING_SNAKE_CASE__       )
							return tile




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  int       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						n % d
							return n - divisor


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def __init__( self     :					Any,			_UpperCAmelCase     :					AutoencoderKL,			_UpperCAmelCase     :					CLIPTextModel,			_UpperCAmelCase     :					CLIPTokenizer,			_UpperCAmelCase     :					UNetaDConditionModel,			_UpperCAmelCase     :					DDPMScheduler,			_UpperCAmelCase     :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],			_UpperCAmelCase     :					int = 3_5_0,			)				->			int:





														"""simple docstring"""
														super().__init__(
														    vae=_UpperCAmelCase,			text_encoder=_UpperCAmelCase,			tokenizer=_UpperCAmelCase,			unet=_UpperCAmelCase,			low_res_scheduler=_UpperCAmelCase,			scheduler=_UpperCAmelCase,			max_noise_level=_UpperCAmelCase,			)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str,			**_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														torch.manual_seed(0						)
														SCREAMING_SNAKE_CASE__       :      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						),
														)
														SCREAMING_SNAKE_CASE__       :      str     =						add_overlap_rect(_UpperCAmelCase,			_UpperCAmelCase,			image.size						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						image.crop(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						translated_slice_x - (original_image_slice / 2)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max(0,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						squeeze_tile(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						to_input.size
														SCREAMING_SNAKE_CASE__       :      str     =						to_input.resize((tile_size, tile_size),			Image.BICUBIC						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						super(_UpperCAmelCase,			self						).__call__(image=_UpperCAmelCase,			**_UpperCAmelCase						).images[0]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4),			Image.BICUBIC						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						unsqueeze_tile(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4),			Image.BICUBIC						)
														SCREAMING_SNAKE_CASE__       :      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"						)
														SCREAMING_SNAKE_CASE__       :      int     =						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     :					int,			_UpperCAmelCase     :					Union[str, List[str]],			_UpperCAmelCase     :					Union[PIL.Image.Image, List[PIL.Image.Image]],			_UpperCAmelCase     :					int = 7_5,			_UpperCAmelCase     :					float = 9.0,			_UpperCAmelCase     :					int = 5_0,			_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 = 1_2_8,			_UpperCAmelCase     :					int = 3_2,			_UpperCAmelCase     :					int = 3_2,			)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						Image.new("RGB",			(image.size[0] * 4, image.size[1] * 4)						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						math.ceil(image.size[0] / tile_size						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						math.ceil(image.size[1] / tile_size						)
														SCREAMING_SNAKE_CASE__       :      str     =						tcx * tcy
														SCREAMING_SNAKE_CASE__       :      Any     =						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 (  )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						"stabilityai/stable-diffusion-x4-upscaler"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE__			,				revision="fp16"			,				torch_dtype=torch.floataa       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						pipe.to("cuda"       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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"       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						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_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






