




"""simple docstring"""


import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					""
 lowercase__ 					=					(
     None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
 )
 lowercase__ 					=					None  # compression type in fsspec. ex: "gzip"
 lowercase__ 					=					None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


 def __init__(				self:		Dict,      a_:		str = "",      a_:		Optional[str] = None,      a_:		Optional[dict] = None,      **a_:		str		):




   '''simple docstring'''



   super().__init__(self,      **a_		)
   # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
   _snake_case							:		Dict								=  fsspec.open(
       a_,      mode="""rb""",      protocol=a_,      compression=self.compression,      client_kwargs={
           """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
           """trust_env""": True,  # Enable reading proxy env variables.
           **(target_options or {}).pop("""client_kwargs""",      {}		),  # To avoid issues if it was already passed.
       },      **(target_options or {}),      )
   _snake_case							:		List[str]								=  os.path.basename(self.file.path.split("""::"""		)[0]		)
   _snake_case							:		List[str]								=  (
       self.compressed_name[: self.compressed_name.rindex("""."""		)]
       if """.""" in self.compressed_name
       else self.compressed_name
   )
   _snake_case							:		List[Any]								=  None


 @classmethod
 def  UpperCamelCase_   (				cls:		Dict,      a_:		List[str]		):




   '''simple docstring'''



   return super()._strip_protocol(a_		).lstrip("""/"""		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   if self.dir_cache is None:
     _snake_case							:		List[Any]								=  {**self.file.fs.info(self.file.path		), """name""": self.uncompressed_name}
     _snake_case							:		Union[str, Any]								=  {f["""name"""]: f}


 def  UpperCamelCase_   (				self:		int,      a_:		str		):




   '''simple docstring'''



   return self.file.open().read()


 def  UpperCamelCase_   (				self:		List[Any],      a_:		str,      a_:		str = "rb",      a_:		str=None,      a_:		Optional[int]=True,      a_:		Any=None,      **a_:		List[Any],      ):




   '''simple docstring'''



   _snake_case							:		str								=  self._strip_protocol(a_		)
   if mode != "rb":
     raise ValueError(f"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"		)
   return self.file.open()







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"bz2"
 lowercase__ 					=					"bz2"
 lowercase__ 					=					".bz2"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"gzip"
 lowercase__ 					=					"gzip"
 lowercase__ 					=					".gz"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"lz4"
 lowercase__ 					=					"lz4"
 lowercase__ 					=					".lz4"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"xz"
 lowercase__ 					=					"xz"
 lowercase__ 					=					".xz"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"zstd"
 lowercase__ 					=					"zstd"
 lowercase__ 					=					".zst"


 def __init__(				self:		List[str],      a_:		str,      a_:		str = "rb",      a_:		Optional[str] = None,      a_:		Optional[dict] = None,      a_:		int = DEFAULT_BLOCK_SIZE,      **a_:		Optional[Any],      ):




   '''simple docstring'''



   super().__init__(
       fo=a_,      mode=a_,      target_protocol=a_,      target_options=a_,      block_size=a_,      **a_,      )
   # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
   #
   # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
   #     out.close = close
   # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
   #
   # see https://github.com/intake/filesystem_spec/issues/725
   _snake_case							:		Any								=  self.file.__enter__






   class 					lowercase:







    '''simple docstring'''





    def __init__(				self:		Union[str, Any],      a_:		List[Any]		):




      '''simple docstring'''



      _snake_case							:		Tuple								=  file_


    def __enter__(				self:		List[str]		):




      '''simple docstring'''



      self._file.__enter__()
      return self


    def __exit__(				self:		int,      *a_:		List[str],      **a_:		Optional[int]		):




      '''simple docstring'''



      self._file.__exit__(*a_,      **a_		)


    def __iter__(				self:		int		):




      '''simple docstring'''



      return iter(self._file		)


    def  UpperCamelCase_   (				self:		Tuple		):




      '''simple docstring'''



      return next(self._file		)


    def __getattr__(				self:		Optional[Any],      a_:		Optional[Any]		):




      '''simple docstring'''



      return getattr(self._file,      a_		)

   def fixed_enter(*a_:		Any,      **a_:		Optional[int]		):
     return WrappedFile(_enter(*a_,      **a_		)		)

   _snake_case							:		Union[str, Any]								=  fixed_enter








"""simple docstring"""


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_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ["""unit tests""", """test file""", """configuration file"""]
  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		Dict								=  0
  _snake_case							:		str								=  0
  # first test
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _snake_case							:		Optional[int]								=  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 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		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 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  tokenizer(example["""content"""]		,   truncation=snake_case__    )["""input_ids"""]
  _snake_case							:		Optional[Any]								=  len(example["""content"""]    ) / len(snake_case__    )
  return {"ratio": ratio}


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  results.update(get_hash(snake_case__    )    )
  results.update(line_stats(snake_case__    )    )
  results.update(alpha_stats(snake_case__    )    )
  results.update(char_token_ratio(snake_case__    )    )
  results.update(is_autogenerated(snake_case__    )    )
  results.update(is_config_or_test(snake_case__    )    )
  results.update(has_no_keywords(snake_case__    )    )
  results.update(has_few_assignments(snake_case__    )    )
  return results


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  if not check_uniques(snake_case__		,   snake_case__    ):
    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 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

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


# Settings
A_						=				HfArgumentParser(PreprocessingArguments)
A_						=				parser.parse_args()
if args.num_workers is None:
     A_						=				multiprocessing.cpu_count()
A_						=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				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_						=				time.time()
     A_	,    A_						=				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_						=				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_						=				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

A_						=				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     A_						=				str(data_dir / F'''file-{file_number+1:012}.json''')
     A_						=				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}''')


