"""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   :	Optional[Any]    					=     re.compile(r'\s+')





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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any			) ->   Optional[int]:
  _lowerCAmelCase		: Optional[int]						    =  [len(_lowerCamelCase			) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(_lowerCamelCase			), "line_max": max(_lowerCamelCase			)}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   Any:
  _lowerCAmelCase		: Tuple						    =  np.mean([c.isalnum() for c in example["""content"""]]			)
  return {"alpha_frac": alpha_frac}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Any			) ->   str:
  if example["hash"] in uniques:
    uniques.remove(example["""hash"""]			)
    return True
  else:
    return False





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Any=5			) ->   int:
  _lowerCAmelCase		: str						    =  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _lowerCAmelCase		: List[str]						    =  example["""content"""].splitlines()
  for _, line in zip(range(_lowerCamelCase			)    ,_lowerCamelCase			):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any=5    ,_lowerCamelCase		: Any=0.05			) ->   Dict:
  _lowerCAmelCase		: Tuple						    =  ["""unit tests""", """test file""", """configuration file"""]
  _lowerCAmelCase		: Any						    =  example["""content"""].splitlines()
  _lowerCAmelCase		: List[Any]						    =  0
  _lowerCAmelCase		: List[str]						    =  0
  # first test
  for _, line in zip(range(_lowerCamelCase			)    ,_lowerCamelCase			):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _lowerCAmelCase		: Tuple						    =  example["""content"""].count("""\n"""			)
  _lowerCAmelCase		: Optional[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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   int:
  _lowerCAmelCase		: Any						    =  ["""def """, """class """, """for """, """while """]
  _lowerCAmelCase		: Optional[int]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Union[str, Any]=4			) ->   str:
  _lowerCAmelCase		: Any						    =  example["""content"""].splitlines()
  _lowerCAmelCase		: Tuple						    =  0
  for line in lines:
    counter += line.lower().count("""="""			)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
  _lowerCAmelCase		: Optional[int]						    =  tokenizer(example["""content"""]    ,truncation=_lowerCamelCase			)["""input_ids"""]
  _lowerCAmelCase		: int						    =  len(example["""content"""]			) / len(_lowerCamelCase			)
  return {"ratio": ratio}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   Dict:
  _lowerCAmelCase		: List[Any]						    =  {}
  results.update(get_hash(_lowerCamelCase			)			)
  results.update(line_stats(_lowerCamelCase			)			)
  results.update(alpha_stats(_lowerCamelCase			)			)
  results.update(char_token_ratio(_lowerCamelCase			)			)
  results.update(is_autogenerated(_lowerCamelCase			)			)
  results.update(is_config_or_test(_lowerCamelCase			)			)
  results.update(has_no_keywords(_lowerCamelCase			)			)
  results.update(has_few_assignments(_lowerCamelCase			)			)
  return results





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: int			) ->   Tuple:
  if not check_uniques(_lowerCamelCase    ,_lowerCamelCase			):
    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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict			) ->   Union[str, Any]:
  with open(_lowerCamelCase    ,"""rb"""			) as f_in:
    with gzip.open(str(_lowerCamelCase			) + """.gz"""    ,"""wb"""    ,compresslevel=6			) as f_out:
      shutil.copyfileobj(_lowerCamelCase    ,_lowerCamelCase			)
  os.unlink(_lowerCamelCase			)


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

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

# Run preprocessing
_a   :	List[Any]    					=     time.time()
_a   :	Any    					=     ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")

# Deduplicate hashes
_a   :	Optional[int]    					=     set(ds.unique('hash'))
_a   :	Dict    					=     len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
_a   :	Dict    					=     time.time()
_a   :	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:
     _a   :	Optional[int]    					=     time.time()
     _a   :	Union[str, 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
_a   :	List[str]    					=     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   :	Dict    					=     output_dir / 'data'
data_dir.mkdir(exist_ok=True)

_a   :	Any    					=     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     _a   :	Union[str, Any]    					=     str(data_dir / F"""file-{file_number+1:012}.json""")
     _a   :	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}""")





"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizer
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizerFast
     _UpperCamelCase							:    Tuple	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    str	      =	False






     def 					__A						(	self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _lowerCAmelCase		: Union[str, Any]						    =  [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
           """<|endoftext|>""",
       ]
       _lowerCAmelCase		: Optional[int]						    =  dict(zip(a__       ,  range(len(a__						)						)						)						)
       _lowerCAmelCase		: str						    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       _lowerCAmelCase		: Any						    =  {"""unk_token""": """<unk>"""}

       _lowerCAmelCase		: List[str]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: List[Any]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write(json.dumps(a__						) + """\n"""						)
       with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write("""\n""".join(a__						)						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: int						    =  """lower newer"""
       _lowerCAmelCase		: List[str]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: List[str]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()
       _lowerCAmelCase		: Optional[int]						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Any						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Any						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: int						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing the unknown token
       _lowerCAmelCase		: List[str]						    =  tokens + [rust_tokenizer.unk_token]
       _lowerCAmelCase		: Dict						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: List[Any]						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Dict						    =  """This is a simple input"""
           _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Optional[int]						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: str						    =  [
               ("""This is a simple input 1""", """This is a simple input 2"""),
               ("""This is a simple pair 1""", """This is a simple pair 2"""),
           ]

           # Simple input tests
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

       # Simple input
       _lowerCAmelCase		: Union[str, Any]						    =  """This is a simple input"""
       _lowerCAmelCase		: Dict						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
       _lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
       _lowerCAmelCase		: Optional[int]						    =  [
           ("""This is a simple input loooooong""", """This is a simple input"""),
           ("""This is a simple pair loooooong""", """This is a simple pair"""),
       ]

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Any						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: str						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["""input_ids"""].shape[-1]       ,  30						)
       self.assertTrue(pad_token_id in out_s["""input_ids"""]						)
       self.assertTrue(0 in out_s["""attention_mask"""]						)

       # s2
       # test automatic padding
       self.assertEqual(out_sa["""input_ids"""].shape[-1]       ,  33						)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]						)
       self.assertFalse(0 in out_sa["""attention_mask"""][0]						)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]						)
       self.assertTrue(0 in out_sa["""attention_mask"""][1]						)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["""input_ids"""].shape[-1]       ,  60						)
       self.assertTrue(pad_token_id in out_p["""input_ids"""]						)
       self.assertTrue(0 in out_p["""attention_mask"""]						)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["""input_ids"""].shape[-1]       ,  52						)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]						)
       self.assertFalse(0 in out_pa["""attention_mask"""][0]						)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]						)
       self.assertTrue(0 in out_pa["""attention_mask"""][1]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  """$$$"""
       _lowerCAmelCase		: str						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: Tuple						    =  """This is a simple input"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: List[str]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: str						    =  tokenizer(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer(a__						)

       self.assertEqual(out_s.input_ids[0]       ,  a__						)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.batch_decode(out_sa.input_ids						)

       self.assertEqual(decode_s.split()[0]       ,  a__						)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       _lowerCAmelCase		: Optional[int]						    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       _lowerCAmelCase		: List[Any]						    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

       _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  ["""^#""", re.escape("""<|endoftext|>"""						), """^'''""", """^\"\"\"""", """\n\n\n"""]
       _lowerCAmelCase		: int						    =  tokenizer.decode(a__       ,  truncate_before_pattern=a__						)
       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       pass




