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


__SCREAMING_SNAKE_CASE						:   int         =			re.compile(R'\s+')
def  snake_case						(__lowercase						)	->	Any:

      '''simple docstring'''



      return {"hash": hashlib.mda(re.sub(__lowercase ,      "" ,      example["content"]						).encode("utf-8"						)						).hexdigest()}
def  snake_case						(__lowercase						)	->	Dict:

      '''simple docstring'''



      _snake_case						:					List[str]    =   [len(__lowercase						) for line in example["content"].splitlines()]
      return {"line_mean": np.mean(__lowercase						), "line_max": max(__lowercase						)}
def  snake_case						(__lowercase						)	->	Any:

      '''simple docstring'''



      _snake_case						:					List[str]    =   np.mean([c.isalnum() for c in example["content"]]						)
      return {"alpha_frac": alpha_frac}
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

      '''simple docstring'''



      if example["hash"] in uniques:
            uniques.remove(example["hash"]						)
            return True
      else:
            return False
def  snake_case						(__lowercase ,      __lowercase=5						)	->	str:

      '''simple docstring'''



      _snake_case						:					Optional[int]    =   ["auto-generated", "autogenerated", "automatically generated"]
      _snake_case						:					int    =   example["content"].splitlines()
      for _, line in zip(range(__lowercase						) ,      __lowercase						):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}
def  snake_case						(__lowercase ,      __lowercase=5 ,      __lowercase=0.05						)	->	List[str]:

      '''simple docstring'''



      _snake_case						:					Dict    =   ["unit tests", "test file", "configuration file"]
      _snake_case						:					Union[str, Any]    =   example["content"].splitlines()
      _snake_case						:					Any    =   0
      _snake_case						:					Dict    =   0
      # first test
      for _, line in zip(range(__lowercase						) ,      __lowercase						):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      _snake_case						:					List[str]    =   example["content"].count("\n"						)
      _snake_case						:					Tuple    =   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  snake_case						(__lowercase						)	->	Tuple:

      '''simple docstring'''



      _snake_case						:					int    =   ["def ", "class ", "for ", "while "]
      _snake_case						:					Dict    =   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  snake_case						(__lowercase ,      __lowercase=4						)	->	Any:

      '''simple docstring'''



      _snake_case						:					str    =   example["content"].splitlines()
      _snake_case						:					Tuple    =   0
      for line in lines:
            counter += line.lower().count("="						)
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}
def  snake_case						(__lowercase						)	->	int:

      '''simple docstring'''



      _snake_case						:					Dict    =   tokenizer(example["content"] ,      truncation=__lowercase						)["input_ids"]
      _snake_case						:					Any    =   len(example["content"]						) / len(__lowercase						)
      return {"ratio": ratio}
def  snake_case						(__lowercase						)	->	Optional[Any]:

      '''simple docstring'''



      _snake_case						:					int    =   {}
      results.update(get_hash(__lowercase						)						)
      results.update(line_stats(__lowercase						)						)
      results.update(alpha_stats(__lowercase						)						)
      results.update(char_token_ratio(__lowercase						)						)
      results.update(is_autogenerated(__lowercase						)						)
      results.update(is_config_or_test(__lowercase						)						)
      results.update(has_no_keywords(__lowercase						)						)
      results.update(has_few_assignments(__lowercase						)						)
      return results
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	List[Any]:

      '''simple docstring'''



      if not check_uniques(__lowercase ,      __lowercase						):
            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  snake_case						(__lowercase						)	->	Dict:

      '''simple docstring'''



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


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

# Load dataset
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			time.time()
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			load_dataset(args.dataset_name, split='train')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
__SCREAMING_SNAKE_CASE						:   Tuple         =			time.time()
__SCREAMING_SNAKE_CASE						:   Optional[int]         =			ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__SCREAMING_SNAKE_CASE						:   str         =			set(ds.unique('hash'))
__SCREAMING_SNAKE_CASE						:   int         =			len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__SCREAMING_SNAKE_CASE						:   str         =			time.time()
__SCREAMING_SNAKE_CASE						:   Dict         =			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:
    __SCREAMING_SNAKE_CASE						:   List[str]         =			time.time()
    __SCREAMING_SNAKE_CASE						:   Any         =			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
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			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)

__SCREAMING_SNAKE_CASE						:   Any         =			output_dir / 'data'
data_dir.mkdir(exist_ok=True)

__SCREAMING_SNAKE_CASE						:   int         =			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    __SCREAMING_SNAKE_CASE						:   Dict         =			str(data_dir / F'''file-{file_number+1:012}.json''')
    __SCREAMING_SNAKE_CASE						:   List[Any]         =			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 importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__SCREAMING_SNAKE_CASE						:   Any         =			importlib.util.find_spec('s3fs') is not None

if _has_safs:
				from .safilesystem import SaFileSystem  # noqa: F401

__SCREAMING_SNAKE_CASE						:   List[compression.BaseCompressedFileFileSystem]         =			[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
				if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
								warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
				fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						if "://" in dataset_path:
												_snake_case						:					Tuple    =   dataset_path.split("://"						)[1]
						return dataset_path
def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   not is_remote_filesystem(__lowercase						)
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(__lowercase						) ,      fs._strip_protocol(__lowercase						)						)
						else:
												fs.mv(__lowercase ,      __lowercase ,      recursive=__lowercase						)

def  snake_case						()	->	None:

						'''simple docstring'''



						if hasattr(fsspec.asyn ,      "reset_lock"						):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												_snake_case						:					List[str]    =   None
												_snake_case						:					str    =   None
												_snake_case						:					List[Any]    =   threading.Lock()