





"""simple docstring"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 					_SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=sys.maxsize							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  '''bilinear'''
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  max_size
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  short_edge_length




       def __call__(						self		,SCREAMING_SNAKE_CASE__							)			->      int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  []
             for img in imgs:
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :List[str]						=  img.shape[:2]
                   # later: provide list and randomly choose index for resize
                   __SCREAMING_SNAKE_CASE   :Dict						=  np.random.randint(self.short_edge_length[0]		,self.short_edge_length[1] + 1							)
                   if size == 0:
                         return img
                   __SCREAMING_SNAKE_CASE   :Tuple						=  size * 1.0 / min(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   if h < w:
                         __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Any						=  size, scale * w
                   else:
                         __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  scale * h, size
                   if max(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) > self.max_size:
                         __SCREAMING_SNAKE_CASE   :List[Any]						=  self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE   :Optional[Any]						=  newh * scale
                         __SCREAMING_SNAKE_CASE   :Tuple						=  neww * scale
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  int(neww + 0.5							)
                   __SCREAMING_SNAKE_CASE   :Dict						=  int(newh + 0.5							)

                   if img.dtype == np.uinta:
                         __SCREAMING_SNAKE_CASE   :List[str]						=  Image.fromarray(SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  pil_image.resize((neww, newh)		,PILImageResampling.BILINEAR							)
                         __SCREAMING_SNAKE_CASE   :str						=  np.asarray(SCREAMING_SNAKE_CASE__							)
                   else:
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  img.permute(2		,0		,1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
                         __SCREAMING_SNAKE_CASE   :str						=  nn.functional.interpolate(
                             SCREAMING_SNAKE_CASE__		,(newh, neww)		,mode=self.interp_method		,align_corners=SCREAMING_SNAKE_CASE__							).squeeze(0							)
                   img_augs.append(SCREAMING_SNAKE_CASE__							)

             return img_augs






class 					_SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,cfg.INPUT.MAX_SIZE_TEST							)
             __SCREAMING_SNAKE_CASE   :Any						=  cfg.INPUT.FORMAT
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  cfg.SIZE_DIVISIBILITY
             __SCREAMING_SNAKE_CASE   :List[str]						=  cfg.PAD_VALUE
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  cfg.INPUT.MAX_SIZE_TEST
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  cfg.MODEL.DEVICE
             __SCREAMING_SNAKE_CASE   :Any						=  torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,1		,1							)
             __SCREAMING_SNAKE_CASE   :Any						=  torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,1		,1							)
             __SCREAMING_SNAKE_CASE   :Dict						=  lambda SCREAMING_SNAKE_CASE__							: (x - self.pixel_mean) / self.pixel_std




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  tuple(max(SCREAMING_SNAKE_CASE__							) for s in zip(*[img.shape for img in images]							)							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  [im.shape[-2:] for im in images]
             __SCREAMING_SNAKE_CASE   :Dict						=  [
                 nn.functional.pad(
                     SCREAMING_SNAKE_CASE__		,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,value=self.pad_value		,)
                 for size, im in zip(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             ]

             return torch.stack(SCREAMING_SNAKE_CASE__							), torch.tensor(SCREAMING_SNAKE_CASE__							)




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False							)			->      Dict:





             """simple docstring"""






             with torch.no_grad():
                   if not isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE   :Dict						=  [images]
                   if single_image:
                         assert len(SCREAMING_SNAKE_CASE__							) == 1
                   for i in range(len(SCREAMING_SNAKE_CASE__							)							):
                         if isinstance(images[i]		,torch.Tensor							):
                               images.insert(SCREAMING_SNAKE_CASE__		,images.pop(SCREAMING_SNAKE_CASE__							).to(self.device							).float()							)
                         elif not isinstance(images[i]		,torch.Tensor							):
                               images.insert(
                                   SCREAMING_SNAKE_CASE__		,torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE__							)		,input_format=self.input_format							)							)
                                   .to(self.device							)
                                   .float()		,)
            # resize smallest edge
                   __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor([im.shape[:2] for im in images]							)
                   __SCREAMING_SNAKE_CASE   :int						=  self.aug(SCREAMING_SNAKE_CASE__							)
                   # transpose images and convert to torch tensors
                   # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                   # now normalize before pad to avoid useless arithmetic
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  [self.normalizer(SCREAMING_SNAKE_CASE__							) for x in images]
                   # now pad them to do the following operations
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :List[str]						=  self.pad(SCREAMING_SNAKE_CASE__							)
                   # Normalize

                   if self.size_divisibility > 0:
                         raise NotImplementedError()
                   # pad
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.true_divide(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   if single_image:
                         return images[0], sizes[0], scales_yx[0]
                   else:
                         return images, sizes, scales_yx




def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict							)			->      Optional[Any]:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes







def      __lowerCamelCase   (							a_     :		Dict ,   a_     :		Tuple[int, int]							)			->      Optional[int]:
      assert torch.isfinite(a_							).all(), "Box tensor contains infinite or NaN!"
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Union[str, Any]						=  box_size
      tensor[:, 0].clamp_(min=0 ,   max=a_							)
      tensor[:, 1].clamp_(min=0 ,   max=a_							)
      tensor[:, 2].clamp_(min=0 ,   max=a_							)
      tensor[:, 3].clamp_(min=0 ,   max=a_							)






"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
      parser.add_argument('''--file_path''' ,   type=a_ ,   default='''data/dump.txt''' ,   help='''The path to the data.'''							)
      parser.add_argument('''--tokenizer_type''' ,   type=a_ ,   default='''bert''' ,   choices=['''bert''', '''roberta''', '''gpt2''']							)
      parser.add_argument('''--tokenizer_name''' ,   type=a_ ,   default='''bert-base-uncased''' ,   help='''The tokenizer to use.'''							)
      parser.add_argument('''--dump_file''' ,   type=a_ ,   default='''data/dump''' ,   help='''The dump file prefix.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

      logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''							)
      if args.tokenizer_type == "bert":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            __SCREAMING_SNAKE_CASE   :str						=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f'''Loading text from {args.file_path}'''							)
      with open(args.file_path ,   '''r''' ,   encoding='''utf8'''							) as fp:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  fp.readlines()

      logger.info('''Start encoding'''							)
      logger.info(f'''{len(a_							)} examples to process.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

            iter += 1
            if iter % interval == 0:
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
                  logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
      logger.info('''Finished binarization'''							)
      logger.info(f'''{len(a_							)} examples processed.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
      __SCREAMING_SNAKE_CASE   :str						=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [np.uintaa(a_							) for d in rslt]
      else:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.intaa(a_							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f'''Dump to {dp_file}'''							)
      with open(a_ ,   '''wb'''							) as handle:
            pickle.dump(rslt_ ,   a_ ,   protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()