




'''simple docstring'''


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_snake_case : Tuple  				=						subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
_snake_case : Optional[Any]  				=						subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()

_snake_case : Optional[int]  				=						'|'.join(sys.argv[1:])
_snake_case : Optional[int]  				=						re.compile(RF'''^({joined_dirs}).*?\.py$''')

_snake_case : List[Any]  				=						[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')







'''simple docstring'''


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_snake_case : Any  				=						'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_snake_case : Any  				=						'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


_snake_case : List[Any]  				=						'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def 						snake_case_       (UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							def remove_articles(UpperCamelCase			:    Optional[int] ):
														_a      	=							re.compile(R'''\b(a|an|the)\b'''					,		re.UNICODE )
														return re.sub(UpperCamelCase					,		''' '''					,		UpperCamelCase )

							def white_space_fix(UpperCamelCase			:    Union[str, Any] ):
														return " ".join(text.split() )

							def remove_punc(UpperCamelCase			:    str ):
														_a      	=							set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(UpperCamelCase			:    Tuple ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase ) ) ) )
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Dict ):





							'''simple docstring'''


							return int(normalize_answer(UpperCamelCase ) == normalize_answer(UpperCamelCase ) )
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							[any(compute_exact(UpperCamelCase					,		UpperCamelCase ) for ref in refs ) for pred, refs in zip(UpperCamelCase					,		UpperCamelCase )]
							return (sum(UpperCamelCase ) / len(UpperCamelCase )) * 100
def 						snake_case_       (UpperCamelCase			:    Any					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    Union[str, Any] ):





							'''simple docstring'''


							_a      	=							[rgram for rgrams in rgramslist for rgram in rgrams]
							_a      	=							Counter(UpperCamelCase )

							_a      	=							Counter(UpperCamelCase )
							_a      	=							Counter()
							for sgram, scount in sgramcounter.items():
														_a      	=							scount * numref

							_a      	=							Counter(UpperCamelCase )
							_a      	=							Counter()
							for cgram, ccount in cgramcounter.items():
														_a      	=							ccount * numref

							# KEEP
							_a      	=							sgramcounter_rep & cgramcounter_rep
							_a      	=							keepgramcounter_rep & rgramcounter
							_a      	=							sgramcounter_rep & rgramcounter

							_a      	=							0
							_a      	=							0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							_a      	=							1
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							keeptmpscorea / len(UpperCamelCase )
							if len(UpperCamelCase ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														_a      	=							keeptmpscorea / sum(keepgramcounterall_rep.values() )
							_a      	=							0
							if keepscore_precision > 0 or keepscore_recall > 0:
														_a      	=							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							_a      	=							sgramcounter_rep - cgramcounter_rep
							_a      	=							delgramcounter_rep - rgramcounter
							_a      	=							sgramcounter_rep - rgramcounter
							_a      	=							0
							_a      	=							0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							deltmpscorea / len(UpperCamelCase )

							# ADDITION
							_a      	=							set(UpperCamelCase ) - set(UpperCamelCase )
							_a      	=							set(UpperCamelCase ) & set(UpperCamelCase )
							_a      	=							set(UpperCamelCase ) - set(UpperCamelCase )

							_a      	=							0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							_a      	=							1
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							addtmpscore / len(UpperCamelCase )
							if len(UpperCamelCase ) > 0:
														_a      	=							addtmpscore / len(UpperCamelCase )
							_a      	=							0
							if addscore_precision > 0 or addscore_recall > 0:
														_a      	=							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)
def 						snake_case_       (UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							len(UpperCamelCase )

							_a      	=							ssent.split(''' ''' )
							_a      	=							csent.split(''' ''' )
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]

							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							for rsent in rsents:
														_a      	=							rsent.split(''' ''' )
														_a      	=							[]
														_a      	=							[]
														_a      	=							[]
														ragramslist.append(UpperCamelCase )
														for i in range(0					,		len(UpperCamelCase ) - 1 ):
																					if i < len(UpperCamelCase ) - 1:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1]
																												ragrams.append(UpperCamelCase )
																					if i < len(UpperCamelCase ) - 2:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																												ragrams.append(UpperCamelCase )
																					if i < len(UpperCamelCase ) - 3:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																												ragrams.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )

							for i in range(0					,		len(UpperCamelCase ) - 1 ):
														if i < len(UpperCamelCase ) - 1:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 2:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 3:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																					sagrams.append(UpperCamelCase )

							for i in range(0					,		len(UpperCamelCase ) - 1 ):
														if i < len(UpperCamelCase ) - 1:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 2:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 3:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(UpperCamelCase )

							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							_a      	=							sum([keepascore, keepascore, keepascore, keepascore] ) / 4
							_a      	=							sum([delascore, delascore, delascore, delascore] ) / 4
							_a      	=							sum([addascore, addascore, addascore, addascore] ) / 4
							_a      	=							(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    str = "13a"					,		UpperCamelCase			:    bool = True ):





							'''simple docstring'''


							if lowercase:
														_a      	=							sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__ ).major >= 2:
																					_a      	=							sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase )()(UpperCamelCase )
														else:
																					_a      	=							sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase )
							elif tokenizer == "moses":
														_a      	=							sacremoses.MosesTokenizer().tokenize(UpperCamelCase					,		return_str=UpperCamelCase					,		escape=UpperCamelCase )
							elif tokenizer == "penn":
														_a      	=							sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase					,		return_str=UpperCamelCase )
							else:
														_a      	=							sentence

							if not return_str:
														_a      	=							normalized_sent.split()

							return normalized_sent
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Dict ):





							'''simple docstring'''


							if not (len(UpperCamelCase ) == len(UpperCamelCase ) == len(UpperCamelCase )):
														raise ValueError('''Sources length must match predictions and references lengths.''' )
							_a      	=							0
							for src, pred, refs in zip(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase ):
														sari_score += SARIsent(normalize(UpperCamelCase )					,		normalize(UpperCamelCase )					,		[normalize(UpperCamelCase ) for sent in refs] )
							_a      	=							sari_score / len(UpperCamelCase )
							return 100 * sari_score
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Tuple					,		UpperCamelCase			:    List[str]="exp"					,		UpperCamelCase			:    List[Any]=None					,		UpperCamelCase			:    Optional[int]=False					,		UpperCamelCase			:    Union[str, Any]=False					,		UpperCamelCase			:    Optional[int]=False					,		):





							'''simple docstring'''


							_a      	=							len(references[0] )
							if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
														raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
							_a      	=							[[refs[i] for refs in references] for i in range(UpperCamelCase )]
							_a      	=							sacrebleu.corpus_bleu(
							    UpperCamelCase					,		UpperCamelCase					,		smooth_method=UpperCamelCase					,		smooth_value=UpperCamelCase					,		force=UpperCamelCase					,		lowercase=UpperCamelCase					,		use_effective_order=UpperCamelCase					,		)
							return output.score

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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														return datasets.MetricInfo(
														    description=_DESCRIPTION				,     citation=_CITATION				,     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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
														        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
														        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
														        '''https://github.com/mjpost/sacreBLEU''',
														    ]				,     reference_urls=[
														        '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
														        '''https://github.com/mjpost/sacreBLEU''',
														        '''https://en.wikipedia.org/wiki/BLEU''',
														        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
														    ]				,     )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							{}
														result.update({'''sari''': compute_sari(sources=lowerCAmelCase_				,     predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														result.update({'''exact''': compute_em(predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														return result

