
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
)
A__   :					Dict							=   logging.getLogger(__name__)




def  _lowerCAmelCase		(		):






  """simple docstring"""



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

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

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

  _lowercase:			Optional[int]       =     []
  _lowercase:			Tuple       =     0
  _lowercase:			int       =     10_000
  _lowercase:			List[Any]       =     time.time()
  for text in data:
    _lowercase:			int       =     f'''{bos} {text.strip()} {sep}'''
    _lowercase:			List[str]       =     tokenizer.encode(_UpperCamelCase ,				add_special_tokens=_UpperCamelCase						)
    rslt.append(_UpperCamelCase						)

    iter += 1
    if iter % interval == 0:
      _lowercase:			Optional[int]       =     time.time()
      logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''						)
      _lowercase:			Dict       =     time.time()
  logger.info('''Finished binarization'''						)
  logger.info(f'''{len(_UpperCamelCase						)} examples processed.'''						)

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


if __name__ == "__main__":
  main()


"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__   :					Dict							=   logging.get_logger(__name__)


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''pixel_values''']
       def __init__(     self ,				A_ = True ,				A_ = None ,				A_ = PILImageResampling.BICUBIC ,				A_ = True ,				A_ = True ,				A_ = 1 / 255 ,				A_ = None ,				A_ = True ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	None:





         """simple docstring"""




         super().__init__(**A_       )
         _lowercase:			List[str]       =     size if size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			Dict       =     get_size_dict(A_       )
         _lowercase:			Any       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			List[str]       =     get_size_dict(A_ ,				default_to_square=A_ ,				param_name='''crop_size'''       )

         _lowercase:			List[Any]       =     do_resize
         _lowercase:			Tuple       =     do_rescale
         _lowercase:			Union[str, Any]       =     do_normalize
         _lowercase:			Optional[int]       =     do_center_crop
         _lowercase:			List[str]       =     crop_size
         _lowercase:			int       =     size
         _lowercase:			List[Any]       =     resample
         _lowercase:			Optional[Any]       =     rescale_factor
         _lowercase:			str       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowercase:			Dict       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = PILImageResampling.BILINEAR ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     get_size_dict(A_       )

         if "shortest_edge" in size:
           _lowercase:			Optional[int]       =     get_resize_output_image_size(A_ ,				size=size['''shortest_edge'''] ,				default_to_square=A_       )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           _lowercase:			Dict       =     (size['''height'''], size['''width'''])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
         return resize(A_ ,				size=A_ ,				resample=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Dict       =     get_size_dict(A_       )
         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(A_ ,				size=(size['''height'''], size['''width''']) ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_       )     ->	np.ndarray:





         """simple docstring"""




         return rescale(A_ ,				scale=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




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



       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = ChannelDimension.FIRST ,				**A_ ,				)     ->	BatchFeature:





         """simple docstring"""




         _lowercase:			List[Any]       =     do_resize if do_resize is not None else self.do_resize
         _lowercase:			Tuple       =     do_rescale if do_rescale is not None else self.do_rescale
         _lowercase:			Optional[Any]       =     do_normalize if do_normalize is not None else self.do_normalize
         _lowercase:			Optional[int]       =     do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase:			Optional[int]       =     crop_size if crop_size is not None else self.crop_size
         _lowercase:			List[Any]       =     get_size_dict(A_ ,				param_name='''crop_size''' ,				default_to_square=A_       )
         _lowercase:			List[str]       =     resample if resample is not None else self.resample
         _lowercase:			List[str]       =     rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase:			List[Any]       =     image_mean if image_mean is not None else self.image_mean
         _lowercase:			List[str]       =     image_std if image_std is not None else self.image_std

         _lowercase:			List[Any]       =     size if size is not None else self.size
         _lowercase:			List[str]       =     get_size_dict(A_       )

         if not is_batched(A_       ):
           _lowercase:			Optional[Any]       =     [images]

         if not valid_images(A_       ):
           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.'''       )

         # All transformations expect numpy arrays.
         _lowercase:			Dict       =     [to_numpy_array(A_       ) for image in images]

         if do_resize:
           _lowercase:			int       =     [self.resize(image=A_ ,				size=A_ ,				resample=A_       ) for image in images]

         if do_center_crop:
           _lowercase:			str       =     [self.center_crop(image=A_ ,				size=A_       ) for image in images]

         if do_rescale:
           _lowercase:			Optional[Any]       =     [self.rescale(image=A_ ,				scale=A_       ) for image in images]

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

         _lowercase:			Optional[Any]       =     [to_channel_dimension_format(A_ ,				A_       ) for image in images]

         _lowercase:			Any       =     {'''pixel_values''': images}
         return BatchFeature(data=A_ ,				tensor_type=A_       )
