



'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase					:  Optional[Any]        =8

def  lowerCamelCase_					(       A_    ,				A_=BITS				):
	__lowerCamelCase		       =		x.device

	__lowerCamelCase		       =		(x * 2_55).int().clamp(0    ,				2_55				)

	__lowerCamelCase		       =		2 ** torch.arange(bits - 1    ,				-1    ,				-1    ,				device=A_				)
	__lowerCamelCase		       =		rearrange(A_    ,				'''d -> d 1 1'''				)
	__lowerCamelCase		       =		rearrange(A_    ,				'''b c h w -> b c 1 h w'''				)

	__lowerCamelCase		       =		((x & mask) != 0).float()
	__lowerCamelCase		       =		rearrange(A_    ,				'''b c d h w -> b (c d) h w'''				)
	__lowerCamelCase		       =		bits * 2 - 1
	return bits

def  lowerCamelCase_					(       A_    ,				A_=BITS				):
	__lowerCamelCase		       =		x.device

	__lowerCamelCase		       =		(x > 0).int()
	__lowerCamelCase		       =		2 ** torch.arange(bits - 1    ,				-1    ,				-1    ,				device=A_    ,				dtype=torch.intaa				)

	__lowerCamelCase		       =		rearrange(A_    ,				'''d -> d 1 1'''				)
	__lowerCamelCase		       =		rearrange(A_    ,				'''b (c d) h w -> b c d h w'''    ,				d=8				)
	__lowerCamelCase		       =		reduce(x * mask    ,				'''b c d h w -> b c h w'''    ,				'''sum'''				)
	return (dec / 2_55).clamp(0.0    ,				1.0				)

def  lowerCamelCase_					(       self    ,				A_    ,				A_    ,				A_    ,				A_ = 0.0    ,				A_ = True    ,				A_=None    ,				A_ = True    ,				):


	if self.num_inference_steps is None:
		raise ValueError(
		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''				)

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	__lowerCamelCase		       =		timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	__lowerCamelCase		       =		self.alphas_cumprod[timestep]
	__lowerCamelCase		       =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	__lowerCamelCase		       =		1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	__lowerCamelCase		       =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	__lowerCamelCase		       =		self.bit_scale
	if self.config.clip_sample:
		__lowerCamelCase		       =		torch.clamp(A_    ,				-scale    ,				A_				)

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	__lowerCamelCase		       =		self._get_variance(A_    ,				A_				)
	__lowerCamelCase		       =		eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		__lowerCamelCase		       =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	__lowerCamelCase		       =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	__lowerCamelCase		       =		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		__lowerCamelCase		       =		model_output.device if torch.is_tensor(A_				) else '''cpu'''
		__lowerCamelCase		       =		torch.randn(model_output.shape    ,				dtype=model_output.dtype    ,				generator=A_				).to(A_				)
		__lowerCamelCase		       =		self._get_variance(A_    ,				A_				) ** 0.5 * eta * noise

		__lowerCamelCase		       =		prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=A_    ,				pred_original_sample=A_				)

def  lowerCamelCase_					(       self    ,				A_    ,				A_    ,				A_    ,				A_="epsilon"    ,				A_=None    ,				A_ = True    ,				):
	__lowerCamelCase		       =		timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		__lowerCamelCase       ,	__lowerCamelCase		       =		torch.split(A_    ,				sample.shape[1]    ,				dim=1				)
	else:
		__lowerCamelCase		       =		None

	# 1. compute alphas, betas
	__lowerCamelCase		       =		self.alphas_cumprod[t]
	__lowerCamelCase		       =		self.alphas_cumprod[t - 1] if t > 0 else self.one
	__lowerCamelCase		       =		1 - alpha_prod_t
	__lowerCamelCase		       =		1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		__lowerCamelCase		       =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		__lowerCamelCase		       =		model_output
	else:
		raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''				)

	# 3. Clip "predicted x_0"
	__lowerCamelCase		       =		self.bit_scale
	if self.config.clip_sample:
		__lowerCamelCase		       =		torch.clamp(A_    ,				-scale    ,				A_				)

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	__lowerCamelCase		       =		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	__lowerCamelCase		       =		self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	__lowerCamelCase		       =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	__lowerCamelCase		       =		0
	if t > 0:
		__lowerCamelCase		       =		torch.randn(
		    model_output.size()    ,				dtype=model_output.dtype    ,				layout=model_output.layout    ,				generator=A_				).to(model_output.device				)
		__lowerCamelCase		       =		(self._get_variance(A_    ,				predicted_variance=A_				) ** 0.5) * noise

	__lowerCamelCase		       =		pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=A_    ,				pred_original_sample=A_				)







class 						_SCREAMING_SNAKE_CASE      ( _a						):






			"""simple docstring"""







			def __init__(   self       , _snake_case       , _snake_case       , _snake_case = 1.0       , ):







				"""simple docstring"""



				super().__init__()
				__lowerCamelCase		       =		bit_scale
				__lowerCamelCase		       =		(
				    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_       , lowerCAmelCase_    ) else ddpm_bit_scheduler_step
				)

				self.register_modules(unet=lowerCAmelCase_       , scheduler=lowerCAmelCase_    )


			@torch.no_grad()
			def __call__(   self       , _snake_case = 2_56       , _snake_case = 2_56       , _snake_case = 50       , _snake_case = None       , _snake_case = 1       , _snake_case = "pil"       , _snake_case = True       , **_snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)       , generator=lowerCAmelCase_       , )
				__lowerCamelCase		       =		decimal_to_bits(lowerCAmelCase_    ) * self.bit_scale
				__lowerCamelCase		       =		latents.to(self.device    )

				self.scheduler.set_timesteps(lowerCAmelCase_    )

				for t in self.progress_bar(self.scheduler.timesteps    ):
					# predict the noise residual
					__lowerCamelCase		       =		self.unet(lowerCAmelCase_       , lowerCAmelCase_    ).sample

					# compute the previous noisy sample x_t -> x_t-1
					__lowerCamelCase		       =		self.scheduler.step(lowerCAmelCase_       , lowerCAmelCase_       , lowerCAmelCase_    ).prev_sample

				__lowerCamelCase		       =		bits_to_decimal(lowerCAmelCase_    )

				if output_type == "pil":
					__lowerCamelCase		       =		self.numpy_to_pil(lowerCAmelCase_    )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=lowerCAmelCase_    )







'''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

