
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


lowercase__          =re.compile(R'\s+')




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 return {"hash": hashlib.mda(re.sub(lowerCAmelCase__  ,       ''''''  ,       example['''content''']  ).encode('''utf-8'''  )  ).hexdigest()}




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 __a			:  int		    =  [len(lowerCAmelCase__  ) for line in example['''content'''].splitlines()]
 return {"line_mean": np.mean(lowerCAmelCase__  ), "line_max": max(lowerCAmelCase__  )}




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):
 __a			:  Optional[int]		    =  np.mean([c.isalnum() for c in example['''content''']]  )
 return {"alpha_frac": alpha_frac}




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]  ):
 if example["hash"] in uniques:
  uniques.remove(example['''hash''']  )
  return True
 else:
  return False




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    List[str]=5  ):
 __a			:  List[Any]		    =  ['''auto-generated''', '''autogenerated''', '''automatically generated''']
 __a			:  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        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[Any]=5  ,       lowerCAmelCase__							:    Optional[Any]=0.05  ):
 __a			:  str		    =  ['''unit tests''', '''test file''', '''configuration file''']
 __a			:  str		    =  example['''content'''].splitlines()
 __a			:  Optional[int]		    =  0
 __a			:  Tuple		    =  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
 __a			:  Any		    =  example['''content'''].count('''\n'''  )
 __a			:  Dict		    =  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					(    lowerCAmelCase__							:    Tuple  ):
 __a			:  Optional[int]		    =  ['''def ''', '''class ''', '''for ''', '''while ''']
 __a			:  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					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    int=4  ):
 __a			:  Tuple		    =  example['''content'''].splitlines()
 __a			:  int		    =  0
 for line in lines:
  counter += line.lower().count('''='''  )
  if counter > minimum:
   return {"has_few_assignments": False}
 return {"has_few_assignments": True}




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 __a			:  Dict		    =  tokenizer(example['''content''']  ,       truncation=lowerCAmelCase__  )['''input_ids''']
 __a			:  Any		    =  len(example['''content''']  ) / len(lowerCAmelCase__  )
 return {"ratio": ratio}




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  Union[str, 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        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    int  ):
 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        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ):
 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
lowercase__          =HfArgumentParser(PreprocessingArguments)
lowercase__          =parser.parse_args()
if args.num_workers is None:
      lowercase__          =multiprocessing.cpu_count()
lowercase__          =AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowercase__          =time.time()
lowercase__          =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:
      lowercase__          =time.time()
      lowercase__       ,      lowercase__          =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
lowercase__          =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)

lowercase__          =output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PhobertTokenizer
   _SCREAMING_SNAKE_CASE				:							int     	=				False
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __a			:  Optional[int]		    =  ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
    __a			:  Tuple		    =  dict(zip(snake_case_ ,    range(len(snake_case_       )       )       )       )
    __a			:  int		    =  ['''#version: 0.2''', '''l à</w>''']
    __a			:  List[Any]		    =  {'''unk_token''': '''<unk>'''}

    __a			:  Any		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  List[Any]		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''merges_file''']       )

    with open(self.vocab_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     for token in vocab_tokens:
      fp.write(f"{token} {vocab_tokens[token]}\n"       )
    with open(self.merges_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     fp.write('''\n'''.join(snake_case_       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    **snake_case_			: List[str]       ):
    kwargs.update(self.special_tokens_map       )
    return PhobertTokenizer.from_pretrained(self.tmpdirname ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Dict       ):
    __a			:  Union[str, Any]		    =  '''Tôi là VinAI Research'''
    __a			:  int		    =  '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
    return input_text, output_text







   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Optional[int]		    =  PhobertTokenizer(self.vocab_file ,    self.merges_file ,    **self.special_tokens_map       )
    __a			:  Any		    =  '''Tôi là VinAI Research'''
    __a			:  Union[str, Any]		    =  '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
    __a			:  List[str]		    =  tokenizer.tokenize(snake_case_       )
    print(snake_case_       )
    self.assertListEqual(snake_case_ ,    snake_case_       )

    __a			:  str		    =  tokens + [tokenizer.unk_token]

    __a			:  str		    =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_       ) ,    snake_case_       )
