




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import CLIPImageProcessor
class 				__snake_case     (unittest.TestCase						):



				def __init__(					self					:						List[str]						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						Dict=7						,		_UpperCAmelCase					:						List[str]=3						,		_UpperCAmelCase					:						Tuple=18						,		_UpperCAmelCase					:						Tuple=30						,		_UpperCAmelCase					:						Any=400						,		_UpperCAmelCase					:						int=True						,		_UpperCAmelCase					:						List[Any]=None						,		_UpperCAmelCase					:						Optional[Any]=True						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						List[str]=[0.48145466, 0.4578275, 0.40821073]						,		_UpperCAmelCase					:						Dict=[0.26862954, 0.26130258, 0.27577711]						,		_UpperCAmelCase					:						Any=True						,		)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					size if size is not None else {"shortest_edge": 20}
								_lowerCAmelCase			:			Tuple        =					crop_size if crop_size is not None else {"height": 18, "width": 18}
								_lowerCAmelCase			:			str        =					parent
								_lowerCAmelCase			:			Any        =					batch_size
								_lowerCAmelCase			:			List[Any]        =					num_channels
								_lowerCAmelCase			:			Any        =					image_size
								_lowerCAmelCase			:			str        =					min_resolution
								_lowerCAmelCase			:			int        =					max_resolution
								_lowerCAmelCase			:			Optional[Any]        =					do_resize
								_lowerCAmelCase			:			Optional[Any]        =					size
								_lowerCAmelCase			:			str        =					do_center_crop
								_lowerCAmelCase			:			Dict        =					crop_size
								_lowerCAmelCase			:			Any        =					do_normalize
								_lowerCAmelCase			:			int        =					image_mean
								_lowerCAmelCase			:			Union[str, Any]        =					image_std
								_lowerCAmelCase			:			Union[str, Any]        =					do_convert_rgb



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			List[Any]:





								'''simple docstring'''




								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple						,		_UpperCAmelCase					:						Union[str, Any]=False						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						int=False					)     ->			List[str]:





								'''simple docstring'''




								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
												_lowerCAmelCase			:			Optional[Any]        =					[]
												for i in range(self.batch_size					):
																image_inputs.append(
																    np.random.randint(
																        255						,		size=(self.num_channels, self.max_resolution, self.max_resolution)						,		dtype=np.uinta					)					)
								else:
												_lowerCAmelCase			:			Tuple        =					[]
												for i in range(self.batch_size					):
																_lowerCAmelCase			:			List[str]        =					np.random.choice(np.arange(self.min_resolution						,		self.max_resolution					)						,		2					)
																image_inputs.append(np.random.randint(255						,		size=(self.num_channels, width, height)						,		dtype=np.uinta					)					)

								if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												_lowerCAmelCase			:			Union[str, Any]        =					[Image.fromarray(np.moveaxis(UpperCAmelCase_						,		0						,		-1					)					) for x in image_inputs]

								if torchify:
												_lowerCAmelCase			:			List[Any]        =					[torch.from_numpy(UpperCAmelCase_					) for x in image_inputs]

								return image_inputs






@require_torch
@require_vision
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   CLIPImageProcessor if is_vision_available() else None



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





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					CLIPImageProcessingTester(self					)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Union[str, Any]:





								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_resize"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""size"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""center_crop"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""image_mean"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""image_std"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_convert_rgb"""					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 20}					)
								self.assertEqual(image_processor.crop_size						,		{"""height""": 18, """width""": 18}					)

								_lowerCAmelCase			:			Any        =					self.image_processing_class.from_dict(self.image_processor_dict						,		size=42						,		crop_size=84					)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 42}					)
								self.assertEqual(image_processor.crop_size						,		{"""height""": 84, """width""": 84}					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			Dict:





								'''simple docstring'''




								pass



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





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_lowerCAmelCase			:			List[str]        =					self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_					)
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase_						,		Image.Image					)

								# Test not batched input
								_lowerCAmelCase			:			Tuple        =					image_processing(image_inputs[0]						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			List[str]        =					image_processing(UpperCAmelCase_						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								_lowerCAmelCase			:			List[Any]        =					self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_						,		numpify=UpperCAmelCase_					)
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase_						,		np.ndarray					)

								# Test not batched input
								_lowerCAmelCase			:			Optional[Any]        =					image_processing(image_inputs[0]						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			Dict        =					image_processing(UpperCAmelCase_						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								_lowerCAmelCase			:			Union[str, Any]        =					self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_						,		torchify=UpperCAmelCase_					)
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase_						,		torch.Tensor					)

								# Test not batched input
								_lowerCAmelCase			:			Union[str, Any]        =					image_processing(image_inputs[0]						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			Union[str, Any]        =					image_processing(UpperCAmelCase_						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)






@require_torch
@require_vision
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   CLIPImageProcessor if is_vision_available() else None



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					CLIPImageProcessingTester(self						,		num_channels=4					)
								_lowerCAmelCase			:			Tuple        =					3



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			int:





								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_resize"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""size"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""center_crop"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""image_mean"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""image_std"""					)					)
								self.assertTrue(hasattr(UpperCAmelCase_						,		"""do_convert_rgb"""					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Optional[int]:





								'''simple docstring'''




								pass



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





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_lowerCAmelCase			:			Tuple        =					self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_					)
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase_						,		Image.Image					)

								# Test not batched input
								_lowerCAmelCase			:			Optional[int]        =					image_processing(image_inputs[0]						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			Dict        =					image_processing(UpperCAmelCase_						,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape						,		(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )						,		)









from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case			 =						"""\
@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},
}
"""

snake_case			 =						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


snake_case			 =						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			[rgram for rgrams in rgramslist for rgram in rgrams]
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter(lowercase )

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for sgram, scount in sgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			scount * numref

  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for cgram, ccount in cgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Tuple		  =			ccount * numref

  # KEEP
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep & cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Dict		  =			keepgramcounter_rep & rgramcounter
  SCREAMING_SNAKE_CASE				:					Tuple		  =			sgramcounter_rep & rgramcounter

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

  # DELETION
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep - cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					Dict		  =			sgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0
  SCREAMING_SNAKE_CASE				:					int		  =			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.
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			deltmpscorea / len(lowercase )

  # ADDITION
  SCREAMING_SNAKE_CASE				:					Dict		  =			set(lowercase ) - set(lowercase )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			set(lowercase ) & set(lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			set(lowercase ) - set(lowercase )

  SCREAMING_SNAKE_CASE				:					Any		  =			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.
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			1
  SCREAMING_SNAKE_CASE				:					Tuple		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			addtmpscore / len(lowercase )
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			addtmpscore / len(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			0
  if addscore_precision > 0 or addscore_recall > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			len(lowercase )

  SCREAMING_SNAKE_CASE				:					Dict		  =			ssent.split(" " )
  SCREAMING_SNAKE_CASE				:					str		  =			csent.split(" " )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]

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

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					List[Any]		  =			sagrams[i] + " " + sagrams[i + 1]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					int		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Dict		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
      sagrams.append(lowercase )

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

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


def      lowerCamelCase__	(   lowercase  ,   lowercase = True  ,   lowercase = "13a"  ,   lowercase = True ):

  """simple docstring"""






  if lowercase:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sentence.lower()

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

  if not return_str:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			normalized_sent.split()

  return normalized_sent


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION    )
class        SCREAMING_SNAKE_CASE	(	datasets.Metric    ):
     '''simple docstring'''


     def  _A				( self				:		Optional[int]					):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,       citation=_CITATION    ,       inputs_description=_KWARGS_DESCRIPTION    ,       features=datasets.Features(
               {
                   "sources": datasets.Value("string"    ,       id="sequence"					),
                   "predictions": datasets.Value("string"    ,       id="sequence"					),
                   "references": datasets.Sequence(datasets.Value("string"    ,       id="sequence"					)    ,       id="references"					),
               }					)    ,       codebase_urls=[
               "https://github.com/cocoxu/simplification/blob/master/SARI.py",
               "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
           ]    ,       reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]    ,       )






     def  _A				( self				:		str    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Optional[int]					):
       if not (len(UpperCAmelCase_					) == len(UpperCAmelCase_					) == len(UpperCAmelCase_					)):
         raise ValueError("Sources length must match predictions and references lengths."					)
       SCREAMING_SNAKE_CASE				:					Any		  =			0
       for src, pred, refs in zip(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					):
         sari_score += SARIsent(normalize(UpperCAmelCase_					)    ,       normalize(UpperCAmelCase_					)    ,       [normalize(UpperCAmelCase_					) for sent in refs]					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			sari_score / len(UpperCAmelCase_					)
       return {"sari": 100 * sari_score}






