


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A   :					Union[str, Any]	=      re.compile(r"\s+")

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      return {"hash": hashlib.mda(re.sub(_A					,      ""					,      example["content"]		).encode("utf-8"		)		).hexdigest()}

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       str					=						[len(_A		) for line in example["content"].splitlines()]
      return {"line_mean": np.mean(_A		), "line_max": max(_A		)}

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						np.mean([c.isalnum() for c in example["content"]]		)
      return {"alpha_frac": alpha_frac}

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Optional[Any]		):

      """simple docstring"""




      if example["hash"] in uniques:
            uniques.remove(example["hash"]		)
            return True
      else:
            return False

def    lowercase_	(	_A    :					Optional[Any]					,      _A    :					Dict=5		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						["auto-generated", "autogenerated", "automatically generated"]
      lowerCamelCase__	:       Tuple					=						example["content"].splitlines()
      for _, line in zip(range(_A		)					,      _A		):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Optional[int]=5					,      _A    :					Optional[int]=0.05		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						["unit tests", "test file", "configuration file"]
      lowerCamelCase__	:       int					=						example["content"].splitlines()
      lowerCamelCase__	:       List[Any]					=						0
      lowerCamelCase__	:       Optional[int]					=						0
      # first test
      for _, line in zip(range(_A		)					,      _A		):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      lowerCamelCase__	:       Any					=						example["content"].count("\n"		)
      lowerCamelCase__	:       List[Any]					=						int(coeff * nlines		)
      for line in lines:
            count_config += line.lower().count("config"		)
            count_test += line.lower().count("test"		)
            if count_config > threshold or count_test > threshold:
                  return {"config_or_test": True}
      return {"config_or_test": False}

def    lowercase_	(	_A    :					List[Any]		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						["def ", "class ", "for ", "while "]
      lowerCamelCase__	:       str					=						example["content"].splitlines()
      for line in lines:
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"has_no_keywords": False}
      return {"has_no_keywords": True}

def    lowercase_	(	_A    :					Tuple					,      _A    :					List[str]=4		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						example["content"].splitlines()
      lowerCamelCase__	:       List[str]					=						0
      for line in lines:
            counter += line.lower().count("="		)
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}

def    lowercase_	(	_A    :					Union[str, Any]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						tokenizer(example["content"]					,      truncation=_A		)["input_ids"]
      lowerCamelCase__	:       List[str]					=						len(example["content"]		) / len(_A		)
      return {"ratio": ratio}

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						{}
      results.update(get_hash(_A		)		)
      results.update(line_stats(_A		)		)
      results.update(alpha_stats(_A		)		)
      results.update(char_token_ratio(_A		)		)
      results.update(is_autogenerated(_A		)		)
      results.update(is_config_or_test(_A		)		)
      results.update(has_no_keywords(_A		)		)
      results.update(has_few_assignments(_A		)		)
      return results

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Dict					,      _A    :					int		):

      """simple docstring"""




      if not check_uniques(_A					,      _A		):
            return False
      elif example["autogenerated"]:
            return False
      elif example["line_max"] > args.line_max:
            return False
      elif example["line_mean"] > args.line_mean:
            return False
      elif example["alpha_frac"] < args.alpha_frac:
            return False
      elif example["ratio"] < args.min_token_ratio:
            return False
      elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
            return False
      elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
            return False
      elif example["has_few_assignments"]:
            return False
      else:
            return True

def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      with open(_A					,      "rb"		) as f_in:
            with gzip.open(str(_A		) + ".gz"					,      "wb"					,      compresslevel=6		) as f_out:
                  shutil.copyfileobj(_A					,      _A		)
      os.unlink(_A		)


# Settings
A   :					str	=      HfArgumentParser(PreprocessingArguments)
A   :					List[str]	=      parser.parse_args()
if args.num_workers is None:
    A   :					Union[str, Any]	=      multiprocessing.cpu_count()
A   :					int	=      AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A   :					str	=      time.time()
A   :					str	=      load_dataset(args.dataset_name, split="train")
print(f'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
A   :					List[str]	=      time.time()
A   :					Dict	=      ds.map(preprocess, num_proc=args.num_workers)
print(f'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
A   :					List[str]	=      set(ds.unique("hash"))
A   :					int	=      len(uniques) / len(ds)
print(f'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
A   :					List[str]	=      time.time()
A   :					List[Any]	=      ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(f'Time to filter dataset: {time.time()-t_start:.2f}')
print(f'Size of filtered dataset: {len(ds_filter)}')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
    A   :					str	=      time.time()
    A,						A   :					List[str]	=      deduplicate_dataset(ds_filter, args.jaccard_threshold)
    print(f'Time to deduplicate dataset: {time.time()-t_start:.2f}')
    print(f'Size of deduplicate dataset: {len(ds_filter)}')

# Save data in batches of samples_per_file
A   :					Optional[int]	=      Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
    with open(output_dir / "duplicate_clusters.json", "w") as f:
        json.dump(duplicate_clusters, f)

A   :					str	=      output_dir / "data"
data_dir.mkdir(exist_ok=True)

A   :					Optional[Any]	=      time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    A   :					Any	=      str(data_dir / f'file-{file_number+1:012}.json')
    A   :					List[str]	=      min(len(ds_filter), index + args.samples_per_file)
    ds_filter.select(list(range(index, end_index))).to_json(file_path)
    compress_file(file_path)


print(f'Time to save dataset: {time.time()-t_start:.2f}')






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


