




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 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Union[str, Any]=[]      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    size[0] - overlap_pixels * 2
				__lowerCamelCase		       =    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
				__lowerCamelCase		       =    np.ones((size_y, size_x) ,     dtype=np.uinta      ) * 255
				__lowerCamelCase		       =    np.pad(lowerCAmelCase_ ,     mode='linear_ramp' ,     pad_width=lowerCAmelCase_ ,     end_values=0      )

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[Any]      )     ->       List[Any]:





				"""simple docstring"""


				return max(lowerCAmelCase_ ,     min(lowerCAmelCase_ ,     lowerCAmelCase_      )      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      [int] ,     UpperCamelCase__				:      [int] ,     UpperCamelCase__				:      [int]      )     ->       str:





				"""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 			lowerCamelCase_		(       UpperCamelCase__				:      [int] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      [int]      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    list(lowerCAmelCase_      )
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				__lowerCamelCase		       =    clamp_rect(lowerCAmelCase_ ,     [0, 0] ,     [image_size[0], image_size[1]]      )
				return rect







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      str      )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    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 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Union[str, Any]      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				__lowerCamelCase		       =    tile.crop(lowerCAmelCase_      )
				return tile







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      str      )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    n % d
				return n - divisor







class     __lowerCAmelCase (       __a ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = 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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							torch.manual_seed(0							)
							__lowerCamelCase		       =    (
							    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							),
							)
							__lowerCamelCase		       =    add_overlap_rect(__a			,						__a			,						image.size							)
							__lowerCamelCase		       =    image.crop(__a							)
							__lowerCamelCase		       =    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
							__lowerCamelCase		       =    translated_slice_x - (original_image_slice / 2)
							__lowerCamelCase		       =    max(0			,						__a							)
							__lowerCamelCase		       =    squeeze_tile(__a			,						__a			,						__a			,						__a							)
							__lowerCamelCase		       =    to_input.size
							__lowerCamelCase		       =    to_input.resize((tile_size, tile_size)			,						Image.BICUBIC							)
							__lowerCamelCase		       =    super(__a			,						self							).__call__(image=__a			,						**__a							).images[0]
							__lowerCamelCase		       =    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,						Image.BICUBIC							)
							__lowerCamelCase		       =    unsqueeze_tile(__a			,						__a							)
							__lowerCamelCase		       =    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,						Image.BICUBIC							)
							__lowerCamelCase		       =    []
							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'							)
							__lowerCamelCase		       =    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			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = 75			,						lowerCamelCase__ = 9.0			,						lowerCamelCase__ = 50			,						lowerCamelCase__ = None			,						lowerCamelCase__ = 1			,						lowerCamelCase__ = 0.0			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = 1			,						lowerCamelCase__ = 128			,						lowerCamelCase__ = 32			,						lowerCamelCase__ = 32			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    Image.new('RGB'			,						(image.size[0] * 4, image.size[1] * 4)							)
							__lowerCamelCase		       =    math.ceil(image.size[0] / tile_size							)
							__lowerCamelCase		       =    math.ceil(image.size[1] / tile_size							)
							__lowerCamelCase		       =    tcx * tcy
							__lowerCamelCase		       =    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 			lowerCamelCase_		(       )     ->       Optional[Any]:





				"""simple docstring"""


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

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

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


if __name__ == "__main__":
					main()










import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase       :       int        =				'''▁'''

lowerCamelCase       :       Optional[int]        =				{'''vocab_file''': '''spiece.model'''}

lowerCamelCase       :       str        =				{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCamelCase       :       str        =				{
    '''google/pegasus-xsum''': 5_12,
}


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

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Tuple     =					VOCAB_FILES_NAMES

					_A  :	Tuple     =					VOCAB_FILES_NAMES
					_A  :	Optional[int]     =					PRETRAINED_VOCAB_FILES_MAP
					_A  :	str     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	Optional[int]     =					['''input_ids''', '''attention_mask''']



					def __init__(     self :		Union[str, Any]						,   __a :		int						,   __a :		Any="<pad>"						,   __a :		Optional[int]="</s>"						,   __a :		Union[str, Any]="<unk>"						,   __a :		Optional[int]="<mask_2>"						,   __a :		Optional[int]="<mask_1>"						,   __a :		Dict=None						,   __a :		List[str]=103						,   __a :		Optional[Dict[str, Any]] = None						,   **__a :		List[Any]						,   )	->     None:


												"""simple docstring"""

												__lowercase					:							Tuple			=   offset
												if additional_special_tokens is not None:
																			if not isinstance(__a						,   __a							):
																										raise TypeError(
																										    F"additional_special_tokens should be of type {type(__a							)}, but is"
																										    F" {type(__a							)}"							)

																			__lowercase					:							Dict			=   (
																			    ([mask_token_sent] + additional_special_tokens)
																			    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																			    else additional_special_tokens
																			)
																			# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																			additional_special_tokens_extended += [
																			    F"<unk_{i}>" for i in range(len(__a							)						,   self.offset - 1							)
																			]

																			if len(set(__a							)							) != len(__a							):
																										raise ValueError(
																										    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																										    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."							)
																			__lowercase					:							Optional[Any]			=   additional_special_tokens_extended
												else:
																			__lowercase					:							int			=   [mask_token_sent] if mask_token_sent is not None else []
																			additional_special_tokens += [F"<unk_{i}>" for i in range(2						,   self.offset							)]

												__lowercase					:							str			=   {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__a						,   unk_token=__a						,   mask_token=__a						,   pad_token=__a						,   mask_token_sent=__a						,   offset=__a						,   additional_special_tokens=__a						,   sp_model_kwargs=self.sp_model_kwargs						,   **__a						,   )
												__lowercase					:							Optional[Any]			=   mask_token_sent
												__lowercase					:							Dict			=   vocab_file
												__lowercase					:							Optional[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(__a							)

												# add special tokens to encoder dict
												__lowercase					:							Dict[int, str]			=   {
												    0: self.pad_token,
												    1: self.eos_token,
												}

												if self.mask_token_sent is not None:
																			self.encoder.update(
																			    {
																			        2: self.mask_token_sent,
																			        3: self.mask_token,
																			    }							)

												if self.offset > 0:
																			# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																			# mask_token_sent is already added to list -> so start at 1
																			self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,   self.offset - 1							)}							)

												__lowercase					:							Dict[str, int]			=   {v: k for k, v in self.encoder.items()}



					@property
					def 					lowerCAmelCase (     self :		Dict							)	->     int:


												"""simple docstring"""

												return len(self.sp_model							) + self.offset



					def 					lowerCAmelCase (     self :		int							)	->     Dict[str, int]:


												"""simple docstring"""

												__lowercase					:							Any			=   {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
												vocab.update(self.added_tokens_encoder							)
												return vocab



					def __getstate__(     self :		List[str]							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   self.__dict__.copy()
												__lowercase					:							Optional[Any]			=   None
												return state



					def __setstate__(     self :		Tuple						,   __a :		Any							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							List[str]			=   d

												# for backward compatibility
												if not hasattr(self						,   """sp_model_kwargs"""							):
																			__lowercase					:							List[str]			=   {}

												__lowercase					:							Optional[int]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(self.vocab_file							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												return self.sp_model.encode(__a						,   out_type=__a							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		str							)	->     int:


												"""simple docstring"""

												if token in self.decoder:
																			return self.decoder[token]
												elif token in self.added_tokens_decoder:
																			return self.added_tokens_decoder[token]
												__lowercase					:							Union[str, Any]			=   self.sp_model.piece_to_id(__a							)
												return sp_id + self.offset



					def 					lowerCAmelCase (     self :		Dict						,   __a :		int							)	->     str:


												"""simple docstring"""

												if index in self.encoder:
																			return self.encoder[index]
												elif index in self.added_tokens_encoder:
																			return self.added_tokens_encoder[index]
												else:
																			__lowercase					:							List[Any]			=   self.sp_model.IdToPiece(index - self.offset							)
												return token



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


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   []
												__lowercase					:							Tuple			=   """"""
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										out_string += self.sp_model.decode(__a							) + token
																										__lowercase					:							str			=   []
																			else:
																										current_sub_tokens.append(__a							)
												out_string += self.sp_model.decode(__a							)
												return out_string.strip()



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[Any]=False							)	->     int:


												"""simple docstring"""

												return 1



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


												"""simple docstring"""

												__lowercase					:							List[Any]			=   set(self.all_special_ids							)  # call it once instead of inside list comp
												all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special

												return [1 if x in all_special_ids else 0 for x in seq]



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		List						,   __a :		Optional[List] = None						,   __a :		bool = False							)	->     List[int]:


												"""simple docstring"""

												if already_has_special_tokens:
																			return self._special_token_mask(__a							)
												elif token_ids_a is None:
																			return self._special_token_mask(__a							) + [1]
												else:
																			return self._special_token_mask(token_ids_a + token_ids_a							) + [1]



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Dict						,   __a :		Tuple=None							)	->     List[int]:


												"""simple docstring"""

												if token_ids_a is None:
																			return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		str						,   __a :		Optional[str] = None							)	->     Tuple[str]:


												"""simple docstring"""

												if not os.path.isdir(__a							):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
																			return
												__lowercase					:							Optional[int]			=   os.path.join(
												    __a						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

												if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							) and os.path.isfile(self.vocab_file							):
																			copyfile(self.vocab_file						,   __a							)
												elif not os.path.isfile(self.vocab_file							):
																			with open(__a						,   """wb"""							) as fi:
																										__lowercase					:							Any			=   self.sp_model.serialized_model_proto()
																										fi.write(__a							)

												return (out_vocab_file,)