import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


__lowerCamelCase     :					Union[str, Any]				  =						logging.getLogger(__name__)



def       __SCREAMING_SNAKE_CASE    (   )				->   Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""      )
  parser.add_argument(
      """--dataset_name"""					,	type=__UpperCamelCase					,	default="""wikitext"""					,	help="""Name of the training. Explore datasets at: hf.co/datasets."""					,	)
  parser.add_argument(
      """--dataset_config"""					,	type=__UpperCamelCase					,	default="""wikitext-103-raw-v1"""					,	help="""Configuration name of the dataset."""      )
  parser.add_argument(
      """--batch_size"""					,	type=__UpperCamelCase					,	default=10_00					,	help="""Batch size during training."""					,	)
  parser.add_argument(
      """--vocab_size"""					,	type=__UpperCamelCase					,	default=1_00_48					,	help="""Size of the desired vocabulary."""					,	)
  parser.add_argument(
      """--limit"""					,	default=__UpperCamelCase					,	type=__UpperCamelCase					,	help="""Limit the number of shards (used for debugging)."""					,	)
  parser.add_argument(
      """--export_to_hub"""					,	action="""store_true"""					,	)

  SCREAMING_SNAKE_CASE__     				= parser.parse_args()
  return args



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int      )				->   List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= datasets.load_dataset(args.dataset_name					,	args.dataset_config					,	split="""train"""      )

  if args.limit is not None:
    SCREAMING_SNAKE_CASE__     				= min(len(__UpperCamelCase      )					,	args.limit      )
    SCREAMING_SNAKE_CASE__     				= dataset.select(range(__UpperCamelCase      )      )
    logger.info(f"""Limiting the dataset to {args.limit} entries."""      )

  def batch_iterator():
    for i in range(0					,	len(__UpperCamelCase      )					,	args.batch_size      ):
      yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
  SCREAMING_SNAKE_CASE__     				= Tokenizer(Unigram()      )
  SCREAMING_SNAKE_CASE__     				= normalizers.Sequence([normalizers.Replace("""``"""					,	"""\""""      ), normalizers.Replace("""''"""					,	"""\""""      )]      )
  SCREAMING_SNAKE_CASE__     				= pre_tokenizers.Metaspace()

  # Prepare the trainer.
  SCREAMING_SNAKE_CASE__     				= UnigramTrainer(
      unk_token="""<unk>"""					,	special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]					,	vocab_size=args.vocab_size					,	)

  logger.info("""Training the tokenizer."""      )
  tokenizer.train_from_iterator(batch_iterator()					,	trainer=__UpperCamelCase      )
  logger.info("""Tokenizer training complete!"""      )

  SCREAMING_SNAKE_CASE__     				= tokenizer.token_to_id("""[CLS]"""      )
  SCREAMING_SNAKE_CASE__     				= tokenizer.token_to_id("""[SEP]"""      )
  SCREAMING_SNAKE_CASE__     				= processors.TemplateProcessing(
      single="""[CLS]:0 $A:0 [SEP]:0"""					,	pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""					,	special_tokens=[
          ("""[CLS]""", cls_token_id),
          ("""[SEP]""", sep_token_id),
      ]					,	)
  SCREAMING_SNAKE_CASE__     				= decoders.Metaspace()

  if args.export_to_hub:
    logger.info("""Exporting the trained tokenzier to Hub."""      )
    SCREAMING_SNAKE_CASE__     				= AlbertTokenizerFast(tokenizer_object=__UpperCamelCase      )
    new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


if __name__ == "__main__":
   __lowerCamelCase     :					str				  =						parse_args()
   main(args)

import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

__lowerCamelCase     :					Optional[Any]				  =						pathlib.Path.cwd().joinpath('''project_euler''')
__lowerCamelCase     :					Dict				  =						pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   __lowerCamelCase     :					dict[str, str]				  =						json.load(file_handle)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   ModuleType:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= importlib.util.spec_from_file_location(file_path.name					,	str(__UpperCamelCase      )      )
  SCREAMING_SNAKE_CASE__     				= importlib.util.module_from_spec(__UpperCamelCase      )  # type: ignore
  spec.loader.exec_module(__UpperCamelCase      )  # type: ignore
  return module



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
    if problem_dir_path.is_file() or problem_dir_path.name.startswith("""_"""      ):
      continue
    for file_path in problem_dir_path.iterdir():
      if file_path.suffix != ".py" or file_path.name.startswith(("""_""", """test""")      ):
        continue
      solution_file_paths.append(__UpperCamelCase      )
  return solution_file_paths



def       __SCREAMING_SNAKE_CASE    (   )				->   str:







  """simple docstring"""





  with open(os.environ["""GITHUB_EVENT_PATH"""]      ) as file:
    SCREAMING_SNAKE_CASE__     				= json.load(__UpperCamelCase      )
  return event["pull_request"]["url"] + "/files"



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  SCREAMING_SNAKE_CASE__     				= {
      """Accept""": """application/vnd.github.v3+json""",
      """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
  }
  SCREAMING_SNAKE_CASE__     				= requests.get(get_files_url()					,	headers=__UpperCamelCase      ).json()
  for file in files:
    SCREAMING_SNAKE_CASE__     				= pathlib.Path.cwd().joinpath(file["""filename"""]      )
    if (
        filepath.suffix != ".py"
        or filepath.name.startswith(("""_""", """test""")      )
        or not filepath.name.startswith("""sol"""      )
    ):
      continue
    solution_file_paths.append(__UpperCamelCase      )
  return solution_file_paths



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  if os.environ.get("""CI"""      ) and os.environ.get("""GITHUB_EVENT_NAME"""      ) == "pull_request":
    # Return only if there are any, otherwise default to all solutions
    if filepaths := added_solution_file_path():
      return filepaths
  return all_solution_file_paths()






@pytest.mark.parametrize(
    """solution_path"""					,	collect_solution_file_paths()					,	ids=lambda __UpperCamelCase      : f"""{path.parent.name}/{path.name}"""					,	)
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   None:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= solution_path.parent.name[8:].zfill(3      )
  SCREAMING_SNAKE_CASE__     				= PROBLEM_ANSWERS[problem_number]
  SCREAMING_SNAKE_CASE__     				= convert_path_to_module(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= str(solution_module.solution()      )  # type: ignore
  SCREAMING_SNAKE_CASE__     				= hashlib.shaaaa(answer.encode()      ).hexdigest()
  assert (
      answer == expected
  ), f"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""
