


'''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   (							__A							:   Optional[int]						,		__A							:   Dict						,		__A							:   Union[str, Any]=[]      ):
				a_		:  Tuple											=      size[0] - overlap_pixels * 2
				a_		:  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
				a_		:  str											=      np.ones((size_y, size_x)						,		dtype=np.uinta      ) * 2_55
				a_		:  int											=      np.pad(__snake_case						,		mode='''linear_ramp'''						,		pad_width=__snake_case						,		end_values=0      )

				if "l" in remove_borders:
								a_		:  Tuple											=      mask[:, overlap_pixels : mask.shape[1]]
				if "r" in remove_borders:
								a_		:  List[str]											=      mask[:, 0 : mask.shape[1] - overlap_pixels]
				if "t" in remove_borders:
								a_		:  Any											=      mask[overlap_pixels : mask.shape[0], :]
				if "b" in remove_borders:
								a_		:  Tuple											=      mask[0 : mask.shape[0] - overlap_pixels, :]
				return mask





def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   List[Any]						,		__A							:   List[str]      ):
				return max(__snake_case						,		min(__snake_case						,		__snake_case      )      )





def 		_UpperCAmelCase   (							__A							:   Any						,		__A							:   List[str]						,		__A							:   str      ):
				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   (							__A							:   Any						,		__A							:   int						,		__A							:   Optional[int]      ):
				a_		:  List[Any]											=      list(__snake_case      )
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				a_		:  List[str]											=      clamp_rect(__snake_case						,		[0, 0]						,		[image_size[0], image_size[1]]      )
				return rect





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Optional[Any]						,		__A							:   str						,		__A							:   Optional[int]      ):
				a_		:  Tuple											=      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(__snake_case						,		(original_slice, 0)      )
				return result





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Tuple      ):
				a_		:  Tuple											=      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				a_		:  int											=      tile.crop(__snake_case      )
				return tile





def 		_UpperCAmelCase   (							__A							:   Optional[int]						,		__A							:   Union[str, Any]      ):
				a_		:  Any											=      n % d
				return n - divisor





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[Any] = 350  ,						)			->			int:
							super().__init__(
							    vae=__lowerCAmelCase  ,						text_encoder=__lowerCAmelCase  ,						tokenizer=__lowerCAmelCase  ,						unet=__lowerCAmelCase  ,						low_res_scheduler=__lowerCAmelCase  ,						scheduler=__lowerCAmelCase  ,						max_noise_level=__lowerCAmelCase  ,						)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						**__SCREAMING_SNAKE_CASE      :			Union[str, Any]					)			->			Optional[Any]:
							torch.manual_seed(0					)
							a_		:  Dict											=      (
							    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					),
							)
							a_		:  int											=      add_overlap_rect(__lowerCAmelCase  ,						__lowerCAmelCase  ,						image.size					)
							a_		:  str											=      image.crop(__lowerCAmelCase					)
							a_		:  List[str]											=      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
							a_		:  Optional[Any]											=      translated_slice_x - (original_image_slice / 2)
							a_		:  Optional[Any]											=      max(0  ,						__lowerCAmelCase					)
							a_		:  List[Any]											=      squeeze_tile(__lowerCAmelCase  ,						__lowerCAmelCase  ,						__lowerCAmelCase  ,						__lowerCAmelCase					)
							a_		:  str											=      to_input.size
							a_		:  int											=      to_input.resize((tile_size, tile_size)  ,						Image.BICUBIC					)
							a_		:  Union[str, Any]											=      super(__lowerCAmelCase  ,						self					).__call__(image=__lowerCAmelCase  ,						**__lowerCAmelCase					).images[0]
							a_		:  Union[str, Any]											=      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)  ,						Image.BICUBIC					)
							a_		:  Optional[Any]											=      unsqueeze_tile(__lowerCAmelCase  ,						__lowerCAmelCase					)
							a_		:  Optional[Any]											=      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)  ,						Image.BICUBIC					)
							a_		:  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'''					)
							a_		:  Optional[int]											=      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      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[str] = 75  ,						__SCREAMING_SNAKE_CASE      :			Dict = 9.0  ,						__SCREAMING_SNAKE_CASE      :			List[str] = 50  ,						__SCREAMING_SNAKE_CASE      :			Tuple = None  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			Any = 0.0  ,						__SCREAMING_SNAKE_CASE      :			Tuple = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any] = None  ,						__SCREAMING_SNAKE_CASE      :			Tuple = None  ,						__SCREAMING_SNAKE_CASE      :			Any = 1  ,						__SCREAMING_SNAKE_CASE      :			List[str] = 128  ,						__SCREAMING_SNAKE_CASE      :			str = 32  ,						__SCREAMING_SNAKE_CASE      :			Tuple = 32  ,						)			->			Optional[Any]:
							a_		:  List[str]											=      Image.new('''RGB'''  ,						(image.size[0] * 4, image.size[1] * 4)					)
							a_		:  Tuple											=      math.ceil(image.size[0] / tile_size					)
							a_		:  Tuple											=      math.ceil(image.size[1] / tile_size					)
							a_		:  str											=      tcx * tcy
							a_		:  Union[str, Any]											=      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   (							):
				a_		:  int											=      '''stabilityai/stable-diffusion-x4-upscaler'''
				a_		:  Any											=      StableDiffusionTiledUpscalePipeline.from_pretrained(__snake_case						,		revision='''fp16'''						,		torch_dtype=torch.floataa      )
				a_		:  Tuple											=      pipe.to('''cuda'''      )
				a_		:  Tuple											=      Image.open('''../../docs/source/imgs/diffusers_library.jpg'''      )

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

				a_		:  Any											=      pipe(image=__snake_case						,		prompt='''Black font, white background, vector'''						,		noise_level=40						,		callback=__snake_case      )
				final_image.save('''diffusers_library.jpg'''      )


if __name__ == "__main__":
		main()












'''simple docstring'''







import os
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 logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict[str, Any]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

							if token_ids_a is None:
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [self.cls_token_id]

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






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															if not prev_is_special:
																			out_string += " "
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

							return (out_vocab_file,)




