




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a__		     =     '''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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__		     =     '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


a__		     =     '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''





def 				A__ (snake_case   :		List[Any]				)     ->						int:

					def remove_articles(snake_case   :		List[str]				):
										__UpperCamelCase					:	str             =      re.compile(r"""\b(a|an|the)\b"""		,	re.UNICODE				)
										return re.sub(snake_case		,	""" """		,	snake_case				)

					def white_space_fix(snake_case   :		List[Any]				):
										return " ".join(text.split()				)

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

					def lower(snake_case   :		int				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(snake_case				)				)				)				)





def 				A__ (snake_case   :		Union[str, Any]		,	snake_case   :		List[Any]				)     ->						Optional[int]:
					return int(normalize_answer(snake_case				) == normalize_answer(snake_case				)				)





def 				A__ (snake_case   :		Union[str, Any]		,	snake_case   :		Tuple				)     ->						Optional[int]:
					__UpperCamelCase					:	List[str]             =      [any(compute_exact(snake_case		,	snake_case				) for ref in refs				) for pred, refs in zip(snake_case		,	snake_case				)]
					return (sum(snake_case				) / len(snake_case				)) * 1_00





def 				A__ (snake_case   :		Union[str, Any]		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		List[Any]				)     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      [rgram for rgrams in rgramslist for rgram in rgrams]
					__UpperCamelCase					:	List[str]             =      Counter(snake_case				)

					__UpperCamelCase					:	Dict             =      Counter(snake_case				)
					__UpperCamelCase					:	str             =      Counter()
					for sgram, scount in sgramcounter.items():
										__UpperCamelCase					:	List[str]             =      scount * numref

					__UpperCamelCase					:	str             =      Counter(snake_case				)
					__UpperCamelCase					:	Union[str, Any]             =      Counter()
					for cgram, ccount in cgramcounter.items():
										__UpperCamelCase					:	Optional[int]             =      ccount * numref

					# KEEP
					__UpperCamelCase					:	Union[str, Any]             =      sgramcounter_rep & cgramcounter_rep
					__UpperCamelCase					:	Any             =      keepgramcounter_rep & rgramcounter
					__UpperCamelCase					:	Optional[int]             =      sgramcounter_rep & rgramcounter

					__UpperCamelCase					:	Optional[int]             =      0
					__UpperCamelCase					:	Dict             =      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.
					__UpperCamelCase					:	Union[str, Any]             =      1
					__UpperCamelCase					:	Tuple             =      1
					if len(snake_case				) > 0:
										__UpperCamelCase					:	Tuple             =      keeptmpscorea / len(snake_case				)
					if len(snake_case				) > 0:
										# Fix an alleged bug [2] in the keep score computation.
										# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
										__UpperCamelCase					:	Optional[int]             =      keeptmpscorea / sum(keepgramcounterall_rep.values()				)
					__UpperCamelCase					:	Optional[int]             =      0
					if keepscore_precision > 0 or keepscore_recall > 0:
										__UpperCamelCase					:	List[str]             =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

					# DELETION
					__UpperCamelCase					:	Tuple             =      sgramcounter_rep - cgramcounter_rep
					__UpperCamelCase					:	List[str]             =      delgramcounter_rep - rgramcounter
					__UpperCamelCase					:	Dict             =      sgramcounter_rep - rgramcounter
					__UpperCamelCase					:	List[str]             =      0
					__UpperCamelCase					:	Any             =      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.
					__UpperCamelCase					:	Optional[Any]             =      1
					if len(snake_case				) > 0:
										__UpperCamelCase					:	str             =      deltmpscorea / len(snake_case				)

					# ADDITION
					__UpperCamelCase					:	Optional[int]             =      set(snake_case				) - set(snake_case				)
					__UpperCamelCase					:	int             =      set(snake_case				) & set(snake_case				)
					__UpperCamelCase					:	Optional[int]             =      set(snake_case				) - set(snake_case				)

					__UpperCamelCase					:	Dict             =      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.
					__UpperCamelCase					:	Dict             =      1
					__UpperCamelCase					:	List[str]             =      1
					if len(snake_case				) > 0:
										__UpperCamelCase					:	Tuple             =      addtmpscore / len(snake_case				)
					if len(snake_case				) > 0:
										__UpperCamelCase					:	Optional[int]             =      addtmpscore / len(snake_case				)
					__UpperCamelCase					:	Union[str, Any]             =      0
					if addscore_precision > 0 or addscore_recall > 0:
										__UpperCamelCase					:	Any             =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

					return (keepscore, delscore_precision, addscore)





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Any		,	snake_case   :		Any				)     ->						int:
					__UpperCamelCase					:	Optional[Any]             =      len(snake_case				)

					__UpperCamelCase					:	str             =      ssent.split(""" """				)
					__UpperCamelCase					:	List[Any]             =      csent.split(""" """				)
					__UpperCamelCase					:	str             =      []
					__UpperCamelCase					:	int             =      []
					__UpperCamelCase					:	int             =      []
					__UpperCamelCase					:	List[Any]             =      []
					__UpperCamelCase					:	List[str]             =      []
					__UpperCamelCase					:	Optional[Any]             =      []

					__UpperCamelCase					:	List[str]             =      []
					__UpperCamelCase					:	str             =      []
					__UpperCamelCase					:	Optional[int]             =      []
					__UpperCamelCase					:	Dict             =      []
					for rsent in rsents:
										__UpperCamelCase					:	List[Any]             =      rsent.split(""" """				)
										__UpperCamelCase					:	int             =      []
										__UpperCamelCase					:	Any             =      []
										__UpperCamelCase					:	Tuple             =      []
										ragramslist.append(snake_case				)
										for i in range(0		,	len(snake_case				) - 1				):
															if i < len(snake_case				) - 1:
																				__UpperCamelCase					:	Tuple             =      ragrams[i] + """ """ + ragrams[i + 1]
																				ragrams.append(snake_case				)
															if i < len(snake_case				) - 2:
																				__UpperCamelCase					:	str             =      ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																				ragrams.append(snake_case				)
															if i < len(snake_case				) - 3:
																				__UpperCamelCase					:	Union[str, Any]             =      ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																				ragrams.append(snake_case				)
										ragramslist.append(snake_case				)
										ragramslist.append(snake_case				)
										ragramslist.append(snake_case				)

					for i in range(0		,	len(snake_case				) - 1				):
										if i < len(snake_case				) - 1:
															__UpperCamelCase					:	List[str]             =      sagrams[i] + """ """ + sagrams[i + 1]
															sagrams.append(snake_case				)
										if i < len(snake_case				) - 2:
															__UpperCamelCase					:	Union[str, Any]             =      sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
															sagrams.append(snake_case				)
										if i < len(snake_case				) - 3:
															__UpperCamelCase					:	int             =      sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
															sagrams.append(snake_case				)

					for i in range(0		,	len(snake_case				) - 1				):
										if i < len(snake_case				) - 1:
															__UpperCamelCase					:	Any             =      cagrams[i] + """ """ + cagrams[i + 1]
															cagrams.append(snake_case				)
										if i < len(snake_case				) - 2:
															__UpperCamelCase					:	int             =      cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
															cagrams.append(snake_case				)
										if i < len(snake_case				) - 3:
															__UpperCamelCase					:	Optional[Any]             =      cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
															cagrams.append(snake_case				)

					((__UpperCamelCase)      ,			(__UpperCamelCase)      ,			(__UpperCamelCase))					:	Any             =      SARIngram(snake_case		,	snake_case		,	snake_case		,	snake_case				)
					((__UpperCamelCase)      ,			(__UpperCamelCase)      ,			(__UpperCamelCase))					:	Optional[int]             =      SARIngram(snake_case		,	snake_case		,	snake_case		,	snake_case				)
					((__UpperCamelCase)      ,			(__UpperCamelCase)      ,			(__UpperCamelCase))					:	List[str]             =      SARIngram(snake_case		,	snake_case		,	snake_case		,	snake_case				)
					((__UpperCamelCase)      ,			(__UpperCamelCase)      ,			(__UpperCamelCase))					:	Tuple             =      SARIngram(snake_case		,	snake_case		,	snake_case		,	snake_case				)
					__UpperCamelCase					:	Any             =      sum([keepascore, keepascore, keepascore, keepascore]				) / 4
					__UpperCamelCase					:	Optional[int]             =      sum([delascore, delascore, delascore, delascore]				) / 4
					__UpperCamelCase					:	int             =      sum([addascore, addascore, addascore, addascore]				) / 4
					__UpperCamelCase					:	Union[str, Any]             =      (avgkeepscore + avgdelscore + avgaddscore) / 3
					return finalscore





def 				A__ (snake_case   :		Optional[Any]		,	snake_case   :		bool = True		,	snake_case   :		str = "13a"		,	snake_case   :		bool = True				)     ->						Tuple:
					# Normalization is requried for the ASSET dataset (one of the primary
					# datasets in sentence simplification) to allow using space
					# to split the sentence. Even though Wiki-Auto and TURK datasets,
					# do not require normalization, we do it for consistency.
					# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
					# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

					if lowercase:
										__UpperCamelCase					:	Dict             =      sentence.lower()

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

					if not return_str:
										__UpperCamelCase					:	Dict             =      normalized_sent.split()

					return normalized_sent





def 				A__ (snake_case   :		Optional[Any]		,	snake_case   :		Any		,	snake_case   :		str				)     ->						str:
					if not (len(snake_case				) == len(snake_case				) == len(snake_case				)):
										raise ValueError("""Sources length must match predictions and references lengths."""				)
					__UpperCamelCase					:	Optional[Any]             =      0
					for src, pred, refs in zip(snake_case		,	snake_case		,	snake_case				):
										sari_score += SARIsent(normalize(snake_case				)		,	normalize(snake_case				)		,	[normalize(snake_case				) for sent in refs]				)
					__UpperCamelCase					:	Optional[int]             =      sari_score / len(snake_case				)
					return 1_00 * sari_score





def 				A__ (snake_case   :		Dict		,	snake_case   :		List[str]		,	snake_case   :		Optional[Any]="exp"		,	snake_case   :		str=None		,	snake_case   :		Any=False		,	snake_case   :		int=False		,	snake_case   :		Optional[Any]=False		,	)     ->						str:
					__UpperCamelCase					:	int             =      len(references[0]				)
					if any(len(snake_case				) != references_per_prediction for refs in references				):
										raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
					__UpperCamelCase					:	Optional[int]             =      [[refs[i] for refs in references] for i in range(snake_case				)]
					__UpperCamelCase					:	Dict             =      sacrebleu.corpus_bleu(
					    snake_case		,	snake_case		,	smooth_method=snake_case		,	smooth_value=snake_case		,	force=snake_case		,	lowercase=snake_case		,	use_effective_order=snake_case		,	)
					return output.score







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



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						str	)	->       Union[str, Any]:







						"""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		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      {}
						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












import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						Tuple	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      data
						__UpperCamelCase					:	List[str]             =      [0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]






	@staticmethod
	def 					lowerCamelCase__     (					lowerCAmelCase		:						str					,     lowerCAmelCase		:						Union[str, Any]	)	->       str:







						"""simple docstring"""







						return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      b"""\x80""" + b"""\x00""" * (63 - (len(self.data	) + 8) % 64)
						__UpperCamelCase					:	int             =      self.data + padding + struct.pack(""">Q"""					,     8 * len(self.data	)	)
						return padded_data






	def 					lowerCamelCase__     (					self		:						str	)	->       str:







						"""simple docstring"""







						return [
						    self.padded_data[i : i + 64] for i in range(0					,     len(self.padded_data	)					,     64	)
						]






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      list(struct.unpack(""">16L"""					,     lowerCAmelCase	)	) + [0] * 64
						for i in range(16					,     80	):
											__UpperCamelCase					:	Dict             =      self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])					,     1	)
						return w






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.padding()
						__UpperCamelCase					:	int             =      self.split_blocks()
						for block in self.blocks:
											__UpperCamelCase					:	Union[str, Any]             =      self.expand_block(lowerCAmelCase	)
											__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	str             =      self.h
											for i in range(0					,     80	):
																if 0 <= i < 20:
																					__UpperCamelCase					:	List[Any]             =      (b & c) | ((~b) & d)
																					__UpperCamelCase					:	str             =      0x5_a_8_2_7_9_9_9
																elif 20 <= i < 40:
																					__UpperCamelCase					:	Tuple             =      b ^ c ^ d
																					__UpperCamelCase					:	List[Any]             =      0x6_e_d_9_e_b_a_1
																elif 40 <= i < 60:
																					__UpperCamelCase					:	Tuple             =      (b & c) | (b & d) | (c & d)
																					__UpperCamelCase					:	List[str]             =      0x8_f_1_b_b_c_d_c
																elif 60 <= i < 80:
																					__UpperCamelCase					:	Optional[int]             =      b ^ c ^ d
																					__UpperCamelCase					:	Tuple             =      0xc_a_6_2_c_1_d_6
																__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      (
																    self.rotate(lowerCAmelCase					,     5	) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																    a,
																    self.rotate(lowerCAmelCase					,     30	),
																    c,
																    d,
																)
											__UpperCamelCase					:	List[Any]             =      (
											    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
											    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
											    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
											    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
											    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
											)
						return ("{:08x}" * 5).format(*self.h	)






def 				A__ ()     ->						int:
					__UpperCamelCase					:	Optional[int]             =      b"""Test String"""
					assert SHAaHash(snake_case				).final_hash() == hashlib.shaa(snake_case				).hexdigest()  # noqa: S324





def 				A__ ()     ->						Tuple:
					__UpperCamelCase					:	Optional[Any]             =      argparse.ArgumentParser(description="""Process some strings or files"""				)
					parser.add_argument(
					    """--string"""		,	dest="""input_string"""		,	default="""Hello World!! Welcome to Cryptography"""		,	help="""Hash the string"""		,	)
					parser.add_argument("""--file"""		,	dest="""input_file"""		,	help="""Hash contents of a file"""				)
					__UpperCamelCase					:	List[Any]             =      parser.parse_args()
					__UpperCamelCase					:	List[str]             =      args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file		,	"""rb"""				) as f:
															__UpperCamelCase					:	Tuple             =      f.read()
					else:
										__UpperCamelCase					:	Any             =      bytes(snake_case		,	"""utf-8"""				)
					print(SHAaHash(snake_case				).final_hash()				)


if __name__ == "__main__":
	main()
	import doctest

	doctest.testmod()






