




"""simple docstring"""







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a      =							'\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'

a      =							'\\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'


a      =							'\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 							lowercase					(snake_case__   : List[str] )     ->  Tuple:


				'''simple docstring'''






				def remove_articles(snake_case__   : str ):
								lowerCAmelCase  =				re.compile(R"""\b(a|an|the)\b"""      ,       re.UNICODE )
								return re.sub(snake_case__      ,       """ """      ,       snake_case__ )

				def white_space_fix(snake_case__   : Any ):
								return " ".join(text.split() )

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

				def lower(snake_case__   : List[str] ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(snake_case__ ) ) ) )





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Union[str, Any] )     ->  Tuple:


				'''simple docstring'''






				return int(normalize_answer(snake_case__ ) == normalize_answer(snake_case__ ) )





def 							lowercase					(snake_case__   : List[str]      ,       snake_case__   : int )     ->  Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase  =				[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__ )) * 100





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[int]      ,       snake_case__   : Any      ,       snake_case__   : Any )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				[rgram for rgrams in rgramslist for rgram in rgrams]
				lowerCAmelCase  =				Counter(snake_case__ )

				lowerCAmelCase  =				Counter(snake_case__ )
				lowerCAmelCase  =				Counter()
				for sgram, scount in sgramcounter.items():
								lowerCAmelCase  =				scount * numref

				lowerCAmelCase  =				Counter(snake_case__ )
				lowerCAmelCase  =				Counter()
				for cgram, ccount in cgramcounter.items():
								lowerCAmelCase  =				ccount * numref

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

				lowerCAmelCase  =				0
				lowerCAmelCase  =				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.
				lowerCAmelCase  =				1
				lowerCAmelCase  =				1
				if len(snake_case__ ) > 0:
								lowerCAmelCase  =				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)
								lowerCAmelCase  =				keeptmpscorea / sum(keepgramcounterall_rep.values() )
				lowerCAmelCase  =				0
				if keepscore_precision > 0 or keepscore_recall > 0:
								lowerCAmelCase  =				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

				# ADDITION
				lowerCAmelCase  =				set(snake_case__ ) - set(snake_case__ )
				lowerCAmelCase  =				set(snake_case__ ) & set(snake_case__ )
				lowerCAmelCase  =				set(snake_case__ ) - set(snake_case__ )

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

				return (keepscore, delscore_precision, addscore)





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Dict      ,       snake_case__   : Union[str, Any] )     ->  Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase  =				len(snake_case__ )

				lowerCAmelCase  =				ssent.split(""" """ )
				lowerCAmelCase  =				csent.split(""" """ )
				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]

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

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





def 							lowercase					(snake_case__   : str      ,       snake_case__   : bool = True      ,       snake_case__   : str = "13a"      ,       snake_case__   : bool = True )     ->  Optional[int]:


				'''simple docstring'''






				if lowercase:
								lowerCAmelCase  =				sentence.lower()

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

				if not return_str:
								lowerCAmelCase  =				normalized_sent.split()

				return normalized_sent





def 							lowercase					(snake_case__   : Any      ,       snake_case__   : Any      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if not (len(snake_case__ ) == len(snake_case__ ) == len(snake_case__ )):
								raise ValueError("""Sources length must match predictions and references lengths.""" )
				lowerCAmelCase  =				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] )
				lowerCAmelCase  =				sari_score / len(snake_case__ )
				return 100 * sari_score





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int      ,       snake_case__   : Optional[Any]="exp"      ,       snake_case__   : Dict=None      ,       snake_case__   : List[str]=False      ,       snake_case__   : Tuple=False      ,       snake_case__   : Tuple=False      ,       )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				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""" )
				lowerCAmelCase  =				[[refs[i] for refs in references] for i in range(snake_case__ )]
				lowerCAmelCase  =				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					):


						def   __lowercase (							self				:			Optional[int]					):
										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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				{}
										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











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





