





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


_lowerCAmelCase 				=     re.compile(R"""\s+""")



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=5     ):
   '''simple docstring'''


   A_     :				Dict			      =	["""auto-generated""", """autogenerated""", """automatically generated"""]
   A_     :				Union[str, Any]			      =	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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=5  ,_lowerCAmelCase=0.05     ):
   '''simple docstring'''


   A_     :				Optional[Any]			      =	["""unit tests""", """test file""", """configuration file"""]
   A_     :				Optional[int]			      =	example["""content"""].splitlines()
   A_     :				Union[str, Any]			      =	0
   A_     :				Union[str, Any]			      =	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_     :				Optional[int]			      =	example["""content"""].count("""\n"""     )
   A_     :				Optional[int]			      =	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     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				int			      =	["""def """, """class """, """for """, """while """]
   A_     :				List[Any]			      =	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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=4     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				str			      =	{}
   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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   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     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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

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

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

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

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

_lowerCAmelCase 				=     output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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













from ....configuration_utils import PretrainedConfig
from ....utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase 				=     {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''retribert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=8      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=True      ,       a__=128      ,       a__=0      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Union[str, Any]			      =	vocab_size
         A_     :				Optional[Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[str]			      =	num_attention_heads
         A_     :				Optional[int]			      =	hidden_act
         A_     :				Any			      =	intermediate_size
         A_     :				Tuple			      =	hidden_dropout_prob
         A_     :				Optional[Any]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[str]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				Optional[Any]			      =	share_encoders
         A_     :				Dict			      =	projection_dim






