


'''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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=[]   ):
			UpperCAmelCase__					:   str       				=       size[0] - overlap_pixels * 2
			UpperCAmelCase__					:   Dict       				=       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
			UpperCAmelCase__					:   Dict       				=       np.ones((size_y, size_x)	, dtype=np.uinta   ) * 2_5_5
			UpperCAmelCase__					:   str       				=       np.pad(UpperCamelCase__	, mode="""linear_ramp"""	, pad_width=UpperCamelCase__	, end_values=0   )

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			return max(UpperCamelCase__	, min(UpperCamelCase__	, UpperCamelCase__   )   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[int]       				=       list(UpperCamelCase__   )
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			UpperCAmelCase__					:   Tuple       				=       clamp_rect(UpperCamelCase__	, [0, 0]	, [image_size[0], image_size[1]]   )
			return rect







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   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(UpperCamelCase__	, (original_slice, 0)   )
			return result







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Tuple       				=       (original_image_slice * 4, 0, tile.size[0], tile.size[1])
			UpperCAmelCase__					:   Dict       				=       tile.crop(UpperCamelCase__   )
			return tile







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Union[str, Any]       				=       n % d
			return n - divisor


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = 350							,							):
								super().__init__(
								    vae=_lowerCamelCase							,							text_encoder=_lowerCamelCase							,							tokenizer=_lowerCamelCase							,							unet=_lowerCamelCase							,							low_res_scheduler=_lowerCamelCase							,							scheduler=_lowerCamelCase							,							max_noise_level=_lowerCamelCase							,							)




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




					@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							,							):
								UpperCAmelCase__					:   Optional[int]       				=       Image.new("""RGB"""							,							(image.size[0] * 4, image.size[1] * 4))
								UpperCAmelCase__					:   Dict       				=       math.ceil(image.size[0] / tile_size)
								UpperCAmelCase__					:   Optional[Any]       				=       math.ceil(image.size[1] / tile_size)
								UpperCAmelCase__					:   Optional[Any]       				=       tcx * tcy
								UpperCAmelCase__					:   str       				=       0
								for y in range(_lowerCamelCase):
											for x in range(_lowerCamelCase):
														self._process_tile(
														    _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							prompt=_lowerCamelCase							,							num_inference_steps=_lowerCamelCase							,							guidance_scale=_lowerCamelCase							,							noise_level=_lowerCamelCase							,							negative_prompt=_lowerCamelCase							,							num_images_per_prompt=_lowerCamelCase							,							eta=_lowerCamelCase							,							generator=_lowerCamelCase							,							latents=_lowerCamelCase							,							)
														current_count += 1
														if callback is not None:
																	callback({"""progress""": current_count / total_tile_count, """image""": final_image})
								return final_image







def       _UpperCamelCase (						):
			# Run a demo
			UpperCAmelCase__					:   Optional[int]       				=       """stabilityai/stable-diffusion-x4-upscaler"""
			UpperCAmelCase__					:   Optional[int]       				=       StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__	, revision="""fp16"""	, torch_dtype=torch.floataa   )
			UpperCAmelCase__					:   Optional[int]       				=       pipe.to("""cuda"""   )
			UpperCAmelCase__					:   Optional[int]       				=       Image.open("""../../docs/source/imgs/diffusers_library.jpg"""   )

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

			UpperCAmelCase__					:   Dict       				=       pipe(image=UpperCamelCase__	, prompt="""Black font, white background, vector"""	, noise_level=4_0	, callback=UpperCamelCase__   )
			final_image.save("""diffusers_library.jpg"""   )


if __name__ == "__main__":
		main()



'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__A		=logging.get_logger(__name__)

__A		={'vocab_file': 'spiece.model'}

__A		={
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<sep>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<cls>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=["<eop>", "<eod>"]							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Dict       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else mask_token

								UpperCAmelCase__					:   Any       				=       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_lowerCamelCase							,							remove_space=_lowerCamelCase							,							keep_accents=_lowerCamelCase							,							bos_token=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)

								UpperCAmelCase__					:   List[str]       				=       3

								UpperCAmelCase__					:   str       				=       do_lower_case
								UpperCAmelCase__					:   Tuple       				=       remove_space
								UpperCAmelCase__					:   List[str]       				=       keep_accents
								UpperCAmelCase__					:   Any       				=       vocab_file

								UpperCAmelCase__					:   str       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_lowerCamelCase)

								try:
											import jieba
								except ModuleNotFoundError as error:
											raise error.__class__(
											    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
											    """See https://pypi.org/project/jieba/ for installation.""")
								UpperCAmelCase__					:   Union[str, Any]       				=       jieba
								UpperCAmelCase__					:   int       				=       str.maketrans(""" \n"""							,							"""\u2582\u2583""")




					@property
					# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
					def 			snake_case__  ( self):
								return len(self.sp_model)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def __getstate__( self):
								UpperCAmelCase__					:   List[str]       				=       self.__dict__.copy()
								UpperCAmelCase__					:   Optional[int]       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__					:   Tuple       				=       {}

								UpperCAmelCase__					:   Union[str, Any]       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if self.remove_space:
											UpperCAmelCase__					:   Tuple       				=       """ """.join(inputs.strip().split())
								else:
											UpperCAmelCase__					:   str       				=       inputs
								UpperCAmelCase__					:   List[Any]       				=       outputs.replace("""``"""							,							"""\"""").replace("""''"""							,							"""\"""")

								if not self.keep_accents:
											UpperCAmelCase__					:   str       				=       unicodedata.normalize("""NFKD"""							,							_lowerCamelCase)
											UpperCAmelCase__					:   Any       				=       """""".join([c for c in outputs if not unicodedata.combining(_lowerCamelCase)])
								if self.do_lower_case:
											UpperCAmelCase__					:   Optional[int]       				=       outputs.lower()

								return outputs




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       self.preprocess_text(_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       []
								for piece in pieces:
											if len(_lowerCamelCase) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
														UpperCAmelCase__					:   str       				=       self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase							,							""""""))
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0]) == 1:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[1:]
																	else:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[0][1:]
														cur_pieces.append(piece[-1])
														new_pieces.extend(_lowerCamelCase)
											else:
														new_pieces.append(_lowerCamelCase)

								return new_pieces




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.PieceToId(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.IdToPiece(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       """""".join(_lowerCamelCase).replace(_lowerCamelCase							,							""" """).strip()
								return out_string




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Optional[Any]       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Optional[int]       				=       [self.cls_token_id]
								if token_ids_a is None:
											return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_lowerCamelCase							,							token_ids_a=_lowerCamelCase							,							already_has_special_tokens=_lowerCamelCase)

								if token_ids_a is not None:
											return ([0] * len(_lowerCamelCase)) + [1] + ([0] * len(_lowerCamelCase)) + [1, 1]
								return ([0] * len(_lowerCamelCase)) + [1, 1]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   int       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Tuple       				=       [2]

								if token_ids_a is None:
											return len(token_ids_a + sep) * [0] + cls_segment_id
								return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   str       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

								return (out_vocab_file,)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       super()._decode(*_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       text.replace(""" """							,							"""""").replace("""\u2582"""							,							""" """).replace("""\u2583"""							,							"""\n""")
								return text