

'''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


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




def 						UpperCamelCase	(			a			)				->     List[Any]:






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




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     __magic_name__						       =    [len(a			) for line in example['''content'''].splitlines()]
     return {"line_mean": np.mean(a			), "line_max": max(a			)}




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    np.mean([c.isalnum() for c in example['''content''']]			)
     return {"alpha_frac": alpha_frac}




def 						UpperCamelCase	(			a		,						a			)				->     Any:






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




def 						UpperCamelCase	(			a		,						a=5			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    ['''auto-generated''', '''autogenerated''', '''automatically generated''']
     __magic_name__						       =    example['''content'''].splitlines()
     for _, line in zip(range(a			)		,						a			):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"autogenerated": True}
     else:
          return {"autogenerated": False}




def 						UpperCamelCase	(			a		,						a=5		,						a=0.05			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    ['''unit tests''', '''test file''', '''configuration file''']
     __magic_name__						       =    example['''content'''].splitlines()
     __magic_name__						       =    0
     __magic_name__						       =    0
     # first test
     for _, line in zip(range(a			)		,						a			):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"config_or_test": True}
    # second test
     __magic_name__						       =    example['''content'''].count('''\n'''			)
     __magic_name__						       =    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	(			a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    ['''def ''', '''class ''', '''for ''', '''while ''']
     __magic_name__						       =    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	(			a		,						a=4			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    example['''content'''].splitlines()
     __magic_name__						       =    0
     for line in lines:
          counter += line.lower().count('''='''			)
          if counter > minimum:
               return {"has_few_assignments": False}
     return {"has_few_assignments": True}




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    tokenizer(example['''content''']		,						truncation=a			)['''input_ids''']
     __magic_name__						       =    len(example['''content''']			) / len(a			)
     return {"ratio": ratio}




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    {}
     results.update(get_hash(a			)			)
     results.update(line_stats(a			)			)
     results.update(alpha_stats(a			)			)
     results.update(char_token_ratio(a			)			)
     results.update(is_autogenerated(a			)			)
     results.update(is_config_or_test(a			)			)
     results.update(has_no_keywords(a			)			)
     results.update(has_few_assignments(a			)			)
     return results




def 						UpperCamelCase	(			a		,						a		,						a			)				->     str:






     '''simple docstring'''
     if not check_uniques(a		,						a			):
          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	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     with open(a		,						'''rb'''			) as f_in:
          with gzip.open(str(a			) + '''.gz'''		,						'''wb'''		,						compresslevel=6			) as f_out:
               shutil.copyfileobj(a		,						a			)
     os.unlink(a			)


# 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}''')









'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






