




"""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  snake_case(    __magic_name__   )			->							str:





     '''simple docstring'''


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






def  snake_case(    __magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Dict       =			[len(lowerCAmelCase__   ) for line in example['''content'''].splitlines()]
     return {"line_mean": np.mean(lowerCAmelCase__   ), "line_max": max(lowerCAmelCase__   )}






def  snake_case(    __magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			np.mean([c.isalnum() for c in example['''content''']]   )
     return {"alpha_frac": alpha_frac}






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[int]:





     '''simple docstring'''


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






def  snake_case(    __magic_name__			,		__magic_name__=5   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			['''auto-generated''', '''autogenerated''', '''automatically generated''']
     lowercase :       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  snake_case(    __magic_name__			,		__magic_name__=5			,		__magic_name__=0.0_5   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[int]       =			['''unit tests''', '''test file''', '''configuration file''']
     lowercase :       Any       =			example['''content'''].splitlines()
     lowercase :       List[Any]       =			0
     lowercase :       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
     lowercase :       List[Any]       =			example['''content'''].count('''\n'''   )
     lowercase :       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  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			['''def ''', '''class ''', '''for ''', '''while ''']
     lowercase :       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  snake_case(    __magic_name__			,		__magic_name__=4   )			->							Optional[int]:





     '''simple docstring'''


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






def  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


     lowercase :       int       =			tokenizer(example['''content''']			,		truncation=lowerCAmelCase__   )['''input_ids''']
     lowercase :       List[str]       =			len(example['''content''']   ) / len(lowerCAmelCase__   )
     return {"ratio": ratio}






def  snake_case(    __magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       Dict       =			{}
     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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							str:





     '''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  snake_case(    __magic_name__   )			->							List[Any]:





     '''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_						=			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 sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(self._get_feature_types()				)				,						reference_urls=[
               '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
           ]				,						)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       if self.config_name == "multilist":
            return {
                "predictions": datasets.Sequence(datasets.Value('''float'''				)				),
                "references": datasets.Sequence(datasets.Value('''float'''				)				),
            }
       else:
            return {
                "predictions": datasets.Value('''float'''				),
                "references": datasets.Value('''float'''				),
            }






  def  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}