






"""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     __lowercase					(    a :				List[str]       ,		a :				List[str]       ,		a :				Any=[] )					->					Optional[int]:
      __snake_case      :    Tuple  			=size[0] - overlap_pixels * 2
      __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
      __snake_case      :    Tuple  			=np.ones((size_y, size_x)       ,		dtype=np.uinta ) * 255
      __snake_case      :    Optional[Any]  			=np.pad(a       ,		mode='''linear_ramp'''       ,		pad_width=a       ,		end_values=0 )

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



def     __lowercase					(    a :				int       ,		a :				Optional[int]       ,		a :				Tuple )					->					Dict:
      return max(a       ,		min(a       ,		a ) )



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



def     __lowercase					(    a :				Optional[int]       ,		a :				Optional[Any]       ,		a :				Union[str, Any]       ,		a :				Union[str, Any] )					->					Dict:
      __snake_case      :    Optional[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(a       ,		(original_slice, 0) )
      return result



def     __lowercase					(    a :				List[str]       ,		a :				Dict )					->					Optional[int]:
      __snake_case      :    Optional[Any]  			=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
      __snake_case      :    str  			=tile.crop(a )
      return tile



def     __lowercase					(    a :				int       ,		a :				Tuple )					->					int:
      __snake_case      :    int  			=n % d
      return n - divisor


class      _lowercase (     lowerCAmelCase							):


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





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	List[str]							,   a				:	int							,   a				:	str							,   a				:	List[Any]							,   a				:	str							,   a				:	Dict							,   a				:	int							,   **a				:	Union[str, Any]						):





            """simple docstring"""







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





            """simple docstring"""







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

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

      __snake_case      :    Optional[Any]  			=pipe(image=a       ,		prompt='''Black font, white background, vector'''       ,		noise_level=40       ,		callback=a )
      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


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

UpperCamelCase_	:				Union[str, Any]										= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_	:				Optional[Any]										= {
    """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"""
        ),
    },
}

UpperCamelCase_	:				int										= {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

UpperCamelCase_	:				Optional[int]										= """▁"""


class      _lowercase (     lowerCAmelCase						):
      _a   :     int								=   VOCAB_FILES_NAMES
      _a   :     int								=   PRETRAINED_VOCAB_FILES_MAP
      _a   :     Union[str, Any]								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a   :     Optional[int]								=   ['''input_ids''', '''attention_mask''']


      def __init__(    self				:	str							,   a				:	Optional[Any]							,   a				:	Optional[Any]="<s>"							,   a				:	Dict="</s>"							,   a				:	Any="</s>"							,   a				:	Optional[int]="<s>"							,   a				:	Optional[Any]="<unk>"							,   a				:	int="<pad>"							,   a				:	Tuple="<mask>"							,   a				:	Optional[Dict[str, Any]] = None							,   **a				:	List[Any]							,   ):





            """simple docstring"""







            __snake_case      :    Dict  			=AddedToken(a							,   lstrip=a							,   rstrip=a						) if isinstance(a							,   a						) else mask_token

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

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

            __snake_case      :    Optional[int]  			=vocab_file
            __snake_case      :    Dict  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(str(a						)						)

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

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


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case      :    Dict  			=[self.cls_token_id]
            __snake_case      :    List[str]  			=[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep


      def _UpperCamelCase						(    self				:	str							,   a				:	List[int]							,   a				:	Optional[List[int]] = None							,   a				:	bool = False						):





            """simple docstring"""







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

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


      def _UpperCamelCase						(    self				:	Dict							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=[self.sep_token_id]
            __snake_case      :    int  			=[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 _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return len(self.sp_model						)


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			={self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
            vocab.update(self.added_tokens_encoder						)
            return vocab


      def _UpperCamelCase						(    self				:	str							,   a				:	str						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	int							,   a				:	Union[str, Any]						):





            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            __snake_case      :    List[Any]  			=self.sp_model.PieceToId(a						)

            return spm_id if spm_id else self.unk_token_id


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Dict						):





            """simple docstring"""







            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=[]
            __snake_case      :    Optional[int]  			=''''''
            __snake_case      :    Optional[int]  			=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(a						) + token
                        __snake_case      :    str  			=True
                        __snake_case      :    int  			=[]
                  else:
                        current_sub_tokens.append(a						)
                        __snake_case      :    Tuple  			=False
            out_string += self.sp_model.decode(a						)
            return out_string.strip()


      def __getstate__(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.__dict__.copy()
            __snake_case      :    Optional[Any]  			=None
            return state


      def __setstate__(    self				:	Optional[int]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=d

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

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







      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	str							,   a				:	Optional[str] = None						):





            """simple docstring"""







            if not os.path.isdir(a						):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                  return
            __snake_case      :    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:
                        __snake_case      :    List[Any]  			=self.sp_model.serialized_model_proto()
                        fi.write(a						)

            return (out_vocab_file,)


