



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A__  :  List[Any]       = '''\
@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},
}
'''

A__  :  Dict       = '''\
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
'''


A__  :  Optional[int]       = '''
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       UpperCamelCase(  __UpperCamelCase							:		List[str]					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		int					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_      :					List[Any]     =			[rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase_      :					str     =			Counter(__UpperCamelCase			)

		lowerCAmelCase_      :					Optional[Any]     =			Counter(__UpperCamelCase			)
		lowerCAmelCase_      :					Dict     =			Counter()
		for sgram, scount in sgramcounter.items():
				lowerCAmelCase_      :					Union[str, Any]     =			scount * numref

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

		# KEEP
		lowerCAmelCase_      :					Optional[Any]     =			sgramcounter_rep & cgramcounter_rep
		lowerCAmelCase_      :					Dict     =			keepgramcounter_rep & rgramcounter
		lowerCAmelCase_      :					Optional[int]     =			sgramcounter_rep & rgramcounter

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

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

		# ADDITION
		lowerCAmelCase_      :					str     =			set(__UpperCamelCase			) - set(__UpperCamelCase			)
		lowerCAmelCase_      :					Union[str, Any]     =			set(__UpperCamelCase			) & set(__UpperCamelCase			)
		lowerCAmelCase_      :					List[Any]     =			set(__UpperCamelCase			) - set(__UpperCamelCase			)

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

		return (keepscore, delscore_precision, addscore)


def       UpperCamelCase(  __UpperCamelCase							:		Tuple					,__UpperCamelCase							:		List[str]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_      :					Optional[int]     =			len(__UpperCamelCase			)

		lowerCAmelCase_      :					str     =			ssent.split(''' '''			)
		lowerCAmelCase_      :					str     =			csent.split(''' '''			)
		lowerCAmelCase_      :					Any     =			[]
		lowerCAmelCase_      :					int     =			[]
		lowerCAmelCase_      :					Optional[int]     =			[]
		lowerCAmelCase_      :					Tuple     =			[]
		lowerCAmelCase_      :					Any     =			[]
		lowerCAmelCase_      :					Optional[int]     =			[]

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

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


def       UpperCamelCase(  __UpperCamelCase							:		Any					,__UpperCamelCase							:		bool = True					,__UpperCamelCase							:		str = "13a"					,__UpperCamelCase							:		bool = True			):
		# 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:
				lowerCAmelCase_      :					Any     =			sentence.lower()

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

		if not return_str:
				lowerCAmelCase_      :					int     =			normalized_sent.split()

		return normalized_sent





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




							def        UpperCAmelCase__      (	self					:	List[Any]):
									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        UpperCAmelCase__      (	self					:	Any    ,    A_					:	List[Any]    ,    A_					:	Optional[int]    ,    A_					:	Any):
									if not (len(A_) == len(A_) == len(A_)):
											raise ValueError('''Sources length must match predictions and references lengths.''')
									lowerCAmelCase_      :					Dict     =			0
									for src, pred, refs in zip(A_    ,    A_    ,    A_):
											sari_score += SARIsent(normalize(A_)    ,    normalize(A_)    ,    [normalize(A_) for sent in refs])
									lowerCAmelCase_      :					Union[str, Any]     =			sari_score / len(A_)
									return {"sari": 1_0_0 * sari_score}










import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

									lowerCAmelCase_      :					Any     =			{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()    ,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,    )
							def        UpperCAmelCase__      (	self					:	List[str]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




							def        UpperCAmelCase__      (	self					:	str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





