






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    :   List[Any]   			=						logging.getLogger(__name__)




def       snake_case  ():







  '''simple docstring'''

  a					:      str    =       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.'				)
  a					:      List[str]    =       parser.parse_args()

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

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

  a					:      List[Any]    =       []
  a					:      Tuple    =       0
  a					:      Optional[Any]    =       1_0_0_0_0
  a					:      Any    =       time.time()
  for text in data:
    a					:      Dict    =       f'''{bos} {text.strip()} {sep}'''
    a					:      List[str]    =       tokenizer.encode(A_     ,      add_special_tokens=A_				)
    rslt.append(A_				)

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

  a					:      List[Any]    =       f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
  a					:      Union[str, Any]    =       tokenizer.vocab_size
  if vocab_size < (1 << 1_6):
    a					:      Any    =       [np.uintaa(A_				) for d in rslt]
  else:
    a					:      str    =       [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()












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




