


'''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


UpperCAmelCase_ :     Any								=      logging.get_logger(__name__)


if is_vision_available():
 import PIL

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						['''pixel_values''']


 def __init__(      self      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = True      ,      __lowercase = 1 / 255      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)
  __A  :		Tuple       =			size if size is not None else {'shortest_edge': 224}
  __A  :		Dict       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
  __A  :		int       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase      ,      param_name='crop_size'						)

  __A  :		Tuple       =			do_resize
  __A  :		Any       =			size
  __A  :		str       =			resample
  __A  :		Any       =			do_center_crop
  __A  :		Union[str, Any]       =			crop_size
  __A  :		Any       =			do_rescale
  __A  :		str       =			rescale_factor
  __A  :		str       =			do_normalize
  __A  :		Tuple       =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  __A  :		str       =			image_std if image_std is not None else OPENAI_CLIP_STD
  __A  :		List[Any]       =			do_convert_rgb


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Any       =			get_size_dict(__lowercase						)
  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(__lowercase      ,      size=(size['height'], size['width'])      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      **__lowercase      ,      ):

  """simple docstring"""







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

  __A  :		List[str]       =			make_list_of_images(__lowercase						)

  if not valid_images(__lowercase						):
   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:
   __A  :		Optional[Any]       =			[convert_to_rgb(__lowercase						) for image in images]

  # All transformations expect numpy arrays.
  __A  :		Any       =			[to_numpy_array(__lowercase						) for image in images]

  if do_resize:
   __A  :		str       =			[self.resize(image=__lowercase      ,      size=__lowercase      ,      resample=__lowercase						) for image in images]

  if do_center_crop:
   __A  :		List[Any]       =			[self.center_crop(image=__lowercase      ,      size=__lowercase						) for image in images]

  if do_rescale:
   __A  :		List[str]       =			[self.rescale(image=__lowercase      ,      scale=__lowercase						) for image in images]

  if do_normalize:
   __A  :		List[str]       =			[self.normalize(image=__lowercase      ,      mean=__lowercase      ,      std=__lowercase						) for image in images]

  __A  :		str       =			[to_channel_dimension_format(__lowercase      ,      __lowercase						) for image in images]

  __A  :		List[str]       =			{'pixel_values': images}
  return BatchFeature(data=__lowercase      ,      tensor_type=__lowercase						)










'''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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


def 			_lowercase       (				):
 __A  :		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.'			)
 __A  :		Tuple       =			parser.parse_args()

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

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

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

 __A  :		List[str]       =			f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
 __A  :		str       =			tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __A  :		Optional[int]       =			[np.uintaa(UpperCamelCase__			) for d in rslt]
 else:
  __A  :		int       =			[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()






