





"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_								=		logging.get_logger(__name__)


if is_vision_available():
  import PIL






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  ['''pixel_values''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 1 / 2_55		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = True		,**SCREAMING_SNAKE_CASE__		,)			->      None:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  size if size is not None else {'''shortest_edge''': 2_24}
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
             __SCREAMING_SNAKE_CASE   :Any						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__		,param_name='''crop_size'''							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  do_resize
             __SCREAMING_SNAKE_CASE   :Dict						=  size
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  resample
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  do_center_crop
             __SCREAMING_SNAKE_CASE   :List[Any]						=  crop_size
             __SCREAMING_SNAKE_CASE   :str						=  do_rescale
             __SCREAMING_SNAKE_CASE   :Tuple						=  rescale_factor
             __SCREAMING_SNAKE_CASE   :str						=  do_normalize
             __SCREAMING_SNAKE_CASE   :List[str]						=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             __SCREAMING_SNAKE_CASE   :List[str]						=  image_std if image_std is not None else OPENAI_CLIP_STD
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  do_convert_rgb




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__							)
             if "shortest_edge" not in size:
                   raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__		,size=size['''shortest_edge''']		,default_to_square=SCREAMING_SNAKE_CASE__							)
             return resize(SCREAMING_SNAKE_CASE__		,size=SCREAMING_SNAKE_CASE__		,resample=SCREAMING_SNAKE_CASE__		,data_format=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  get_size_dict(SCREAMING_SNAKE_CASE__							)
             if "height" not in size or "width" not in size:
                   raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
             return center_crop(SCREAMING_SNAKE_CASE__		,size=(size['''height'''], size['''width'''])		,data_format=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      int:





             """simple docstring"""






             return rescale(SCREAMING_SNAKE_CASE__		,scale=SCREAMING_SNAKE_CASE__		,data_format=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      np.ndarray:





             """simple docstring"""






             return normalize(SCREAMING_SNAKE_CASE__		,mean=SCREAMING_SNAKE_CASE__		,std=SCREAMING_SNAKE_CASE__		,data_format=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST		,**SCREAMING_SNAKE_CASE__		,)			->      PIL.Image.Image:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  do_resize if do_resize is not None else self.do_resize
             __SCREAMING_SNAKE_CASE   :str						=  size if size is not None else self.size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  get_size_dict(SCREAMING_SNAKE_CASE__		,param_name='''size'''		,default_to_square=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  resample if resample is not None else self.resample
             __SCREAMING_SNAKE_CASE   :Dict						=  do_center_crop if do_center_crop is not None else self.do_center_crop
             __SCREAMING_SNAKE_CASE   :List[str]						=  crop_size if crop_size is not None else self.crop_size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  get_size_dict(SCREAMING_SNAKE_CASE__		,param_name='''crop_size'''		,default_to_square=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  do_rescale if do_rescale is not None else self.do_rescale
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
             __SCREAMING_SNAKE_CASE   :int						=  do_normalize if do_normalize is not None else self.do_normalize
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image_mean if image_mean is not None else self.image_mean
             __SCREAMING_SNAKE_CASE   :Dict						=  image_std if image_std is not None else self.image_std
             __SCREAMING_SNAKE_CASE   :int						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             __SCREAMING_SNAKE_CASE   :List[str]						=  make_list_of_images(SCREAMING_SNAKE_CASE__							)

             if not valid_images(SCREAMING_SNAKE_CASE__							):
                   raise ValueError(
                       '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                       '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

             if do_resize and size is None:
                   raise ValueError('''Size must be specified if do_resize is True.'''							)

             if do_center_crop and crop_size is None:
                   raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

             if do_rescale and rescale_factor is None:
                   raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  [convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

             # All transformations expect numpy arrays.
             __SCREAMING_SNAKE_CASE   :Tuple						=  [to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

             if do_resize:
                   __SCREAMING_SNAKE_CASE   :Dict						=  [self.resize(image=SCREAMING_SNAKE_CASE__		,size=SCREAMING_SNAKE_CASE__		,resample=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_center_crop:
                   __SCREAMING_SNAKE_CASE   :Dict						=  [self.center_crop(image=SCREAMING_SNAKE_CASE__		,size=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_rescale:
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  [self.rescale(image=SCREAMING_SNAKE_CASE__		,scale=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_normalize:
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  [self.normalize(image=SCREAMING_SNAKE_CASE__		,mean=SCREAMING_SNAKE_CASE__		,std=SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {'''pixel_values''': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__		,tensor_type=SCREAMING_SNAKE_CASE__							)






"""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()