




'''simple docstring'''



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_							:	List[str]	     =		"\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"

UpperCAmelCase_							:	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"


UpperCAmelCase_							:	Union[str, 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       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> List[Any]:




     '''simple docstring'''





     def remove_articles(_lowerCamelCase				:  str						):
          __snake_case            =	re.compile(R'''\b(a|an|the)\b''' ,						re.UNICODE						)
          return re.sub(_UpperCamelCase ,						''' ''' ,						_UpperCamelCase						)

     def white_space_fix(_lowerCamelCase				:  str						):
          return " ".join(text.split()						)

     def remove_punc(_lowerCamelCase				:  Any						):
          __snake_case            =	set(string.punctuation						)
          return "".join(ch for ch in text if ch not in exclude						)

     def lower(_lowerCamelCase				:  Union[str, Any]						):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase						)						)						)						)







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[str]						)-> Dict:




     '''simple docstring'''





     return int(normalize_answer(_UpperCamelCase						) == normalize_answer(_UpperCamelCase						)						)







def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[Any]						)-> int:




     '''simple docstring'''





     __snake_case            =	[any(compute_exact(_UpperCamelCase ,						_UpperCamelCase						) for ref in refs						) for pred, refs in zip(_UpperCamelCase ,						_UpperCamelCase						)]
     return (sum(_UpperCamelCase						) / len(_UpperCamelCase						)) * 1_00







def       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Optional[int]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[rgram for rgrams in rgramslist for rgram in rgrams]
     __snake_case            =	Counter(_UpperCamelCase						)

     __snake_case            =	Counter(_UpperCamelCase						)
     __snake_case            =	Counter()
     for sgram, scount in sgramcounter.items():
          __snake_case            =	scount * numref

     __snake_case            =	Counter(_UpperCamelCase						)
     __snake_case            =	Counter()
     for cgram, ccount in cgramcounter.items():
          __snake_case            =	ccount * numref

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

     __snake_case            =	0
     __snake_case            =	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.
     __snake_case            =	1
     __snake_case            =	1
     if len(_UpperCamelCase						) > 0:
          __snake_case            =	keeptmpscorea / len(_UpperCamelCase						)
     if len(_UpperCamelCase						) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          __snake_case            =	keeptmpscorea / sum(keepgramcounterall_rep.values()						)
     __snake_case            =	0
     if keepscore_precision > 0 or keepscore_recall > 0:
          __snake_case            =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     __snake_case            =	sgramcounter_rep - cgramcounter_rep
     __snake_case            =	delgramcounter_rep - rgramcounter
     __snake_case            =	sgramcounter_rep - rgramcounter
     __snake_case            =	0
     __snake_case            =	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.
     __snake_case            =	1
     if len(_UpperCamelCase						) > 0:
          __snake_case            =	deltmpscorea / len(_UpperCamelCase						)

     # ADDITION
     __snake_case            =	set(_UpperCamelCase						) - set(_UpperCamelCase						)
     __snake_case            =	set(_UpperCamelCase						) & set(_UpperCamelCase						)
     __snake_case            =	set(_UpperCamelCase						) - set(_UpperCamelCase						)

     __snake_case            =	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.
     __snake_case            =	1
     __snake_case            =	1
     if len(_UpperCamelCase						) > 0:
          __snake_case            =	addtmpscore / len(_UpperCamelCase						)
     if len(_UpperCamelCase						) > 0:
          __snake_case            =	addtmpscore / len(_UpperCamelCase						)
     __snake_case            =	0
     if addscore_precision > 0 or addscore_recall > 0:
          __snake_case            =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Any						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	len(_UpperCamelCase						)

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

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

     ((__snake_case)       ,     (__snake_case)       ,     (__snake_case))            =	SARIngram(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase						)
     ((__snake_case)       ,     (__snake_case)       ,     (__snake_case))            =	SARIngram(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase						)
     ((__snake_case)       ,     (__snake_case)       ,     (__snake_case))            =	SARIngram(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase						)
     ((__snake_case)       ,     (__snake_case)       ,     (__snake_case))            =	SARIngram(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase						)
     __snake_case            =	sum([keepascore, keepascore, keepascore, keepascore]						) / 4
     __snake_case            =	sum([delascore, delascore, delascore, delascore]						) / 4
     __snake_case            =	sum([addascore, addascore, addascore, addascore]						) / 4
     __snake_case            =	(avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  int = True ,						_lowerCamelCase				:  Optional[Any] = "13a" ,						_lowerCamelCase				:  str = True						)-> Dict:




     '''simple docstring'''





     if lowercase:
          __snake_case            =	sentence.lower()

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

     if not return_str:
          __snake_case            =	normalized_sent.split()

     return normalized_sent







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any]						)-> Union[str, Any]:




     '''simple docstring'''





     if not (len(_UpperCamelCase						) == len(_UpperCamelCase						) == len(_UpperCamelCase						)):
          raise ValueError('''Sources length must match predictions and references lengths.'''						)
     __snake_case            =	0
     for src, pred, refs in zip(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase						):
          sari_score += SARIsent(normalize(_UpperCamelCase						) ,						normalize(_UpperCamelCase						) ,						[normalize(_UpperCamelCase						) for sent in refs]						)
     __snake_case            =	sari_score / len(_UpperCamelCase						)
     return 1_00 * sari_score







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[Any]="exp" ,						_lowerCamelCase				:  Tuple=None ,						_lowerCamelCase				:  Any=False ,						_lowerCamelCase				:  Union[str, Any]=False ,						_lowerCamelCase				:  Tuple=False ,						)-> str:




     '''simple docstring'''





     __snake_case            =	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'''						)
     __snake_case            =	[[refs[i] for refs in references] for i in range(_UpperCamelCase						)]
     __snake_case            =	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      lowerCAmelCase (			datasets.Metric):


       def    lowerCAmelCase   ( self	)   ->	str:



            '''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     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




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










"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [False] * len(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			queue.append(_UpperCamelCase )
			__lowerCAmelCase							       =     True

			while queue:
						__lowerCAmelCase							       =     queue.pop(0 )
						for ind in range(len(graph[u] ) ):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(_UpperCamelCase )
												__lowerCAmelCase							       =     True
												__lowerCAmelCase							       =     u

			return visited[t]


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [-1] * (len(_UpperCamelCase ))
			__lowerCAmelCase							       =     0
			while bfs(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):
						__lowerCAmelCase							       =     float("Inf" )
						__lowerCAmelCase							       =     sink

						while s != source:
									# Find the minimum value in select path
									__lowerCAmelCase							       =     min(_UpperCamelCase       ,					graph[parent[s]][s] )
									__lowerCAmelCase							       =     parent[s]

						max_flow += path_flow
						__lowerCAmelCase							       =     sink

						while v != source:
									__lowerCAmelCase							       =     parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									__lowerCAmelCase							       =     parent[v]
			return max_flow


A	:  Union[str, Any]            =     [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

A       ,       A	:  Any            =     0, 5


print(ford_fulkerson(graph, source, sink))




