'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			int   ,		__snake_case							:			Dict=1_0_2_4				):
     _A		,       _A    		=  [], []

     _A    		=  list(zip(__snake_case   ,		__snake_case				)				)
     _A		,       _A    		=  sorted_examples[0]

     def is_too_big(__snake_case							:			int				):
          return tok(__snake_case   ,		return_tensors='pt'				).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]				):
          _A    		=  new_src + ' ' + src
          _A    		=  new_tgt + ' ' + tgt
          if is_too_big(__snake_case				) or is_too_big(__snake_case				):  # cant fit, finalize example
               finished_src.append(__snake_case				)
               finished_tgt.append(__snake_case				)
               _A		,       _A    		=  src, tgt
          else:  # can fit, keep adding
               _A		,       _A    		=  cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(__snake_case				)
          finished_tgt.append(__snake_case				)
     return finished_src, finished_tgt





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			Path   ,		__snake_case							:			str   ,		__snake_case							:			Union[str, Any]				):
     _A    		=  Path(__snake_case				)
     save_path.mkdir(exist_ok=__snake_case				)
     for split in ["train"]:
          _A		,       _A    		=  data_dir / F'{split}.source', data_dir / F'{split}.target'
          _A    		=  [x.rstrip() for x in Path(__snake_case				).open().readlines()]
          _A    		=  [x.rstrip() for x in Path(__snake_case				).open().readlines()]
          _A		,       _A    		=  pack_examples(__snake_case   ,		__snake_case   ,		__snake_case   ,		__snake_case				)
          print(F'packed {split} split from {len(__snake_case				)} examples -> {len(__snake_case				)}.'				)
          Path(save_path / F'{split}.source'				).open('w'				).write('\n'.join(__snake_case				)				)
          Path(save_path / F'{split}.target'				).open('w'				).write('\n'.join(__snake_case				)				)
     for split in ["val", "test"]:
          _A		,       _A    		=  data_dir / F'{split}.source', data_dir / F'{split}.target'
          shutil.copyfile(__snake_case   ,		save_path / F'{split}.source'				)
          shutil.copyfile(__snake_case   ,		save_path / F'{split}.target'				)





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  argparse.ArgumentParser()
     parser.add_argument('--tok_name'   ,		type=__snake_case   ,		help='like facebook/bart-large-cnn,t5-base, etc.'				)
     parser.add_argument('--max_seq_len'   ,		type=__snake_case   ,		default=1_2_8				)
     parser.add_argument('--data_dir'   ,		type=__snake_case				)
     parser.add_argument('--save_path'   ,		type=__snake_case				)
     _A    		=  parser.parse_args()
     _A    		=  AutoTokenizer.from_pretrained(args.tok_name				)
     return pack_data_dir(__snake_case   ,		Path(args.data_dir				)   ,		args.max_seq_len   ,		args.save_path				)


if __name__ == "__main__":
   packer_cli()




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def _lowerCamelCase  (		snake_case  ):
							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'     , snake_case     , )
							if isinstance(snake_case     , torch.Tensor  ):
														return image
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[image]

							if isinstance(image[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							image[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






def _lowerCamelCase  (		snake_case  ):
							if isinstance(snake_case     , torch.Tensor  ):
														return mask
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[mask]

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

										_lowerCAmelCase							=							_preprocess_image(lowercase__						)
										_lowerCAmelCase							=							original_image.to(device=self.device  ,  dtype=self.unet.dtype						)
										_lowerCAmelCase							=							_preprocess_mask(lowercase__						)
										_lowerCAmelCase							=							mask_image.to(device=self.device  ,  dtype=self.unet.dtype						)

										_lowerCAmelCase							=							original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(lowercase__  ,  lowercase__						) and len(lowercase__						) != batch_size:
																	raise ValueError(
																	    f'You have passed a list of generators of length {len(lowercase__						)}, but requested an effective batch'
																	    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

										_lowerCAmelCase							=							original_image.shape
										_lowerCAmelCase							=							randn_tensor(lowercase__  ,  generator=lowercase__  ,  device=self.device  ,  dtype=self.unet.dtype						)

										# set step values
										self.scheduler.set_timesteps(lowercase__  ,  lowercase__  ,  lowercase__  ,  self.device						)
										_lowerCAmelCase							=							eta

										_lowerCAmelCase							=							self.scheduler.timesteps[0] + 1
										_lowerCAmelCase							=							generator[0] if isinstance(lowercase__  ,  lowercase__						) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																	if t < t_last:
																								# predict the noise residual
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						).sample
																								# compute previous image: x_t -> x_t-1
																								_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

																	else:
																								# compute the reverse: x_t-1 -> x_t
																								_lowerCAmelCase							=							self.scheduler.undo_step(lowercase__  ,  lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							t

										_lowerCAmelCase							=							(image / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										if output_type == "pil":
																	_lowerCAmelCase							=							self.numpy_to_pil(lowercase__						)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






