


"""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


a__				:							int						=     re.compile(r'''\s+''')




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_=5   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				["auto-generated", "autogenerated", "automatically generated"]
					__SCREAMING_SNAKE_CASE						   =				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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_=5  ,						lowerCAmelCase_=0.05   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				["unit tests", "test file", "configuration file"]
					__SCREAMING_SNAKE_CASE						   =				example["content"].splitlines()
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				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
					__SCREAMING_SNAKE_CASE						   =				example["content"].count("\n"   )
					__SCREAMING_SNAKE_CASE						   =				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__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				["def ", "class ", "for ", "while "]
					__SCREAMING_SNAKE_CASE						   =				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__       (lowerCAmelCase_  ,						lowerCAmelCase_=4   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tokenizer(example["content"]  ,						truncation=lowerCAmelCase_   )["input_ids"]
					__SCREAMING_SNAKE_CASE						   =				len(example["content"]   ) / len(lowerCAmelCase_   )
					return {"ratio": ratio}




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				{}
					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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''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
a__				:							List[str]						=     HfArgumentParser(PreprocessingArguments)
a__				:							List[str]						=     parser.parse_args()
if args.num_workers is None:
		a__				:							Tuple						=     multiprocessing.cpu_count()
a__				:							int						=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
a__				:							Tuple						=     set(ds.unique('''hash'''))
a__				:							Union[str, Any]						=     len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
a__				:							Optional[Any]						=     time.time()
a__				:							str						=     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:
		a__				:							str						=     time.time()
		a__			,     a__				:							str						=     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
a__				:							Dict						=     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)

a__				:							List[str]						=     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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




"""simple docstring"""







from jiwer import compute_measures

import datasets


a__				:							Optional[int]						=     '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

a__				:							List[str]						=     '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

a__				:							Dict						=     '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	str:
						return datasets.MetricInfo(
						    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"			,			id="sequence"   ),
						            "references": datasets.Value("string"			,			id="sequence"   ),
						        }   )			,			codebase_urls=["https://github.com/jitsi/jiwer/"]			,			reference_urls=[
						        "https://en.wikipedia.org/wiki/Word_error_rate",
						    ]			,			)




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				Tuple=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				Any=False   )       ->	Optional[int]:
						if concatenate_texts:
											return compute_measures(UpperCAmelCase__			,			UpperCAmelCase__   )["wer"]
						else:
											__SCREAMING_SNAKE_CASE						   =				0
											__SCREAMING_SNAKE_CASE						   =				0
											for prediction, reference in zip(UpperCAmelCase__			,			UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				compute_measures(UpperCAmelCase__			,			UpperCAmelCase__   )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total
