

"""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
)
_a							=							logging.getLogger(__name__)




def   _A  (  )     ->						List[Any]:
   '''simple docstring'''


   __lowercase								=						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								=						parser.parse_args()

   logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""")
   if args.tokenizer_type == "bert":
      __lowercase								=						BertTokenizer.from_pretrained(args.tokenizer_name)
      __lowercase								=						tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
      __lowercase								=						tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      __lowercase								=						RobertaTokenizer.from_pretrained(args.tokenizer_name)
      __lowercase								=						tokenizer.special_tokens_map["cls_token"]  # `<s>`
      __lowercase								=						tokenizer.special_tokens_map["sep_token"]  # `</s>`
   elif args.tokenizer_type == "gpt2":
      __lowercase								=						GPTaTokenizer.from_pretrained(args.tokenizer_name)
      __lowercase								=						tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
      __lowercase								=						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								=						fp.readlines()

   logger.info("Start encoding")
   logger.info(F"""{len(UpperCamelCase_)} examples to process.""")

   __lowercase								=						[]
   __lowercase								=						0
   __lowercase								=						10000
   __lowercase								=						time.time()
   for text in data:
      __lowercase								=						F"""{bos} {text.strip()} {sep}"""
      __lowercase								=						tokenizer.encode(UpperCamelCase_,      add_special_tokens=UpperCamelCase_)
      rslt.append(UpperCamelCase_)

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

   __lowercase								=						F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
   __lowercase								=						tokenizer.vocab_size
   if vocab_size < (1 << 16):
      __lowercase								=						[np.uintaa(UpperCamelCase_) for d in rslt]
   else:
      __lowercase								=						[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"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

_a				,						_a							=							os.path.split(__file__)
_a							=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()




