





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


_snake_case  :    Optional[Any]						    =  re.compile(R'\s+')

def   a_    ( lowerCAmelCase_      :			str	):
   return {"hash": hashlib.mda(re.sub(lowerCAmelCase_,	'',	example['content']	).encode('utf-8'	)	).hexdigest()}

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	[len(lowerCAmelCase_	) for line in example['content'].splitlines()]
   return {"line_mean": np.mean(lowerCAmelCase_	), "line_max": max(lowerCAmelCase_	)}

def   a_    ( lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	np.mean([c.isalnum() for c in example['content']]	)
   return {"alpha_frac": alpha_frac}

def   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Optional[Any]	):
   if example["hash"] in uniques:
      uniques.remove(example['hash']	)
      return True
   else:
      return False

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

def   a_    ( lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	tokenizer(example['content'],	truncation=lowerCAmelCase_	)['input_ids']
   __lowerCAmelCase								=	len(example['content']	) / len(lowerCAmelCase_	)
   return {"ratio": ratio}

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	{}
   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   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Dict	):
   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   a_    ( lowerCAmelCase_      :			List[str]	):
   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
_snake_case  :    Tuple						    =  HfArgumentParser(PreprocessingArguments)
_snake_case  :    List[str]						    =  parser.parse_args()
if args.num_workers is None:
     _snake_case  :    Union[str, Any]						    =  multiprocessing.cpu_count()
_snake_case  :    Dict						    =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_snake_case  :    List[str]						    =  time.time()
_snake_case  :    Optional[int]						    =  load_dataset(args.dataset_name, split='train')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")

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

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

# Deduplicate data and apply heuristics
_snake_case  :    Any						    =  time.time()
_snake_case  :    Union[str, Any]						    =  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:
     _snake_case  :    str						    =  time.time()
     _snake_case    , _snake_case  :    Optional[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
_snake_case  :    List[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)

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

