






'''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_  			=			re.compile(r'\s+')



def       __UpperCAmelCase					(lowercase__					)		->					Optional[int]:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__					)		->					List[str]:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__					)		->					str:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__						,lowercase__					)		->					Dict:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__						,lowercase__=5					)		->					int:

						'''simple docstring'''


						a_					  = ["auto-generated", "autogenerated", "automatically generated"]
						a_					  = 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					(lowercase__						,lowercase__=5						,lowercase__=0.05					)		->					int:

						'''simple docstring'''


						a_					  = ["unit tests", "test file", "configuration file"]
						a_					  = example["content"].splitlines()
						a_					  = 0
						a_					  = 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
						a_					  = example["content"].count("\n"					)
						a_					  = 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					(lowercase__					)		->					int:

						'''simple docstring'''


						a_					  = ["def ", "class ", "for ", "while "]
						a_					  = 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					(lowercase__						,lowercase__=4					)		->					Union[str, Any]:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__					)		->					Tuple:

						'''simple docstring'''


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



def       __UpperCAmelCase					(lowercase__					)		->					Optional[Any]:

						'''simple docstring'''


						a_					  = {}
						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					(lowercase__						,lowercase__						,lowercase__					)		->					List[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       __UpperCAmelCase					(lowercase__					)		->					Optional[int]:

						'''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_  			=			HfArgumentParser(PreprocessingArguments)
a_  			=			parser.parse_args()
if args.num_workers is None:
							a_  			=			multiprocessing.cpu_count()
a_  			=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
a_  			=			time.time()
a_  			=			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_  			=			time.time()
							a_			,     a_  			=			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_  			=			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_  			=			output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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





import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__A							='''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__A							='''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

__A							='''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					if version.parse(scb.__version__					) < version.parse("1.4.12"					):
									raise ImportWarning(
									    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
									    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."					)
					return datasets.MetricInfo(
					    description=_DESCRIPTION		,    citation=_CITATION		,    homepage="http://www.cs.umd.edu/~snover/tercom/"		,    inputs_description=_KWARGS_DESCRIPTION		,    features=datasets.Features(
					        {
					            "predictions": datasets.Value("string"		,    id="sequence"					),
					            "references": datasets.Sequence(datasets.Value("string"		,    id="sequence"					)		,    id="references"					),
					        }					)		,    codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]		,    reference_urls=[
					        "https://github.com/jhclark/tercom",
					    ]		,    )



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    )							->			Tuple:
					lowerCamelCase_         =     len(references[0]					)
					if any(len(lowercase					) != references_per_prediction for refs in references					):
									raise ValueError("Sacrebleu requires the same number of references for each prediction"					)
					lowerCamelCase_         =     [[refs[i] for refs in references] for i in range(lowercase					)]

					lowerCamelCase_         =     TER(
					    normalized=lowercase		,    no_punct=lowercase		,    asian_support=lowercase		,    case_sensitive=lowercase		,    )
					lowerCamelCase_         =     sb_ter.corpus_score(lowercase		,    lowercase					)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}


