



from scipy.stats import spearmanr

import datasets


UpperCAmelCase__     = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

UpperCAmelCase__     = "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

UpperCAmelCase__     = r"\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION		)
class     __lowerCAmelCase					(    datasets.Metric		):



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						return datasets.MetricInfo(
						    description=_DESCRIPTION				,	citation=_CITATION				,	inputs_description=_KWARGS_DESCRIPTION				,	features=datasets.Features(
						        {
						            'predictions': datasets.Value('float'),
						            'references': datasets.Value('float'),
						        })				,	reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html']				,	)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Tuple				,	A     :						Union[str, Any]				,	A     :						Union[str, Any]=False)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     spearmanr(A				,	A)
						if return_pvalue:
									return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
									return {"spearmanr": results[0]}





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


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





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				str:







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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







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





def 							A			(  _UpperCAmelCase       :     Any					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[Any]					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=5					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Tuple=5							,     _UpperCAmelCase       :     Optional[int]=0.05					)       ->				Tuple:







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







			'''simple docstring'''
			_UpperCAmelCase	 =     ['def ', 'class ', 'for ', 'while ']
			_UpperCAmelCase	 =     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			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[Any]=4					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				str:







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





def 							A			(  _UpperCAmelCase       :     Dict					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Any:







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







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


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

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

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

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

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

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

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