
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


_lowercase											=    re.compile(R"""\s+""")
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]				)			->						Optional[int]:
       return {"hash": hashlib.mda(re.sub(UpperCAmelCase_			,							''''''			,							example['''content''']				).encode('''utf-8'''				)				).hexdigest()}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]				)			->						str:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[len(UpperCAmelCase_				) for line in example['''content'''].splitlines()]
       return {"line_mean": np.mean(UpperCAmelCase_				), "line_max": max(UpperCAmelCase_				)}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict				)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=np.mean([c.isalnum() for c in example['''content''']]				)
       return {"alpha_frac": alpha_frac}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict				)			->						int:
       if example["hash"] in uniques:
              uniques.remove(example['''hash''']				)
              return True
       else:
              return False
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Optional[int]=5				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''auto-generated''', '''autogenerated''', '''automatically generated''']
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict=5			,							UpperCAmelCase_	:     List[Any]=0.05				)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=['''unit tests''', '''test file''', '''configuration file''']
       SCREAMING_SNAKE_CASE_							:				List[str]		=example['''content'''].splitlines()
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       SCREAMING_SNAKE_CASE_							:				str		=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
       SCREAMING_SNAKE_CASE_							:				int		=example['''content'''].count('''\n'''				)
       SCREAMING_SNAKE_CASE_							:				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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]				)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				int		=['''def ''', '''class ''', '''for ''', '''while ''']
       SCREAMING_SNAKE_CASE_							:				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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     Any=4				)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				str		=example['''content'''].splitlines()
       SCREAMING_SNAKE_CASE_							:				int		=0
       for line in lines:
              counter += line.lower().count('''='''				)
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Any		=tokenizer(example['''content''']			,							truncation=UpperCAmelCase_				)['''input_ids''']
       SCREAMING_SNAKE_CASE_							:				int		=len(example['''content''']				) / len(UpperCAmelCase_				)
       return {"ratio": ratio}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				List[Any]		={}
       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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Tuple				)			->						List[Any]:
       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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str				)			->						Optional[int]:
       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
_lowercase											=    HfArgumentParser(PreprocessingArguments)
_lowercase											=    parser.parse_args()
if args.num_workers is None:
 _lowercase											=    multiprocessing.cpu_count()
_lowercase											=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

_lowercase											=    time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
 _lowercase											=    str(data_dir / F"file-{file_number+1:012}.json")
 _lowercase											=    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 scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_lowercase											=    """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

_lowercase											=    """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

_lowercase											=    """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Union[str, Any]				)			->						int:
       return float((preds == labels).mean()				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		=simple_accuracy(UpperCAmelCase_			,							UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=float(fa_score(y_true=UpperCAmelCase_			,							y_pred=UpperCAmelCase_				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				str		=float(pearsonr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=float(spearmanr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }


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


       def 	_snake_case				(    self				)						->    str:
              if self.config_name not in [
                  "sst2",
                  "mnli",
                  "mnli_mismatched",
                  "mnli_matched",
                  "cola",
                  "stsb",
                  "mrpc",
                  "qqp",
                  "qnli",
                  "rte",
                  "wnli",
                  "hans",
              ]:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       inputs_description=_KWARGS_DESCRIPTION						,       features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                          '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                      }				)						,       codebase_urls=[]						,       reference_urls=[]						,       format='''numpy'''						,       )





       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[Any]:
              if self.config_name == "cola":
                     return {"matthews_correlation": matthews_corrcoef(__A						,       __A				)}
              elif self.config_name == "stsb":
                     return pearson_and_spearman(__A						,       __A				)
              elif self.config_name in ["mrpc", "qqp"]:
                     return acc_and_fa(__A						,       __A				)
              elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                     return {"accuracy": simple_accuracy(__A						,       __A				)}
              else:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
