






"""simple docstring"""


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCamelCase				:    List[str]     =						'\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

_UpperCamelCase				:    List[Any]     =						'\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


_UpperCamelCase				:    Tuple     =						'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'




def 			snake_case  (A_      :List[Any]	,			A_      :int	,			A_      :Any	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			Tuple				=   [rgram for rgrams in rgramslist for rgram in rgrams]
  a     :			List[Any]				=   Counter(A_      )

  a     :			Dict				=   Counter(A_      )
  a     :			Any				=   Counter()
  for sgram, scount in sgramcounter.items():
    a     :			int				=   scount * numref

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

  # KEEP
  a     :			Tuple				=   sgramcounter_rep & cgramcounter_rep
  a     :			List[str]				=   keepgramcounter_rep & rgramcounter
  a     :			List[Any]				=   sgramcounter_rep & rgramcounter

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

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

  # ADDITION
  a     :			Optional[int]				=   set(A_      ) - set(A_      )
  a     :			Dict				=   set(A_      ) & set(A_      )
  a     :			Optional[Any]				=   set(A_      ) - set(A_      )

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

  return (keepscore, delscore_precision, addscore)




def 			snake_case  (A_      :Tuple	,			A_      :str	,			A_      :Optional[Any]      ):







  '''simple docstring'''

  a     :			Dict				=   len(A_      )

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

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

  for i in range(0	,			len(A_      ) - 1      ):
    if i < len(A_      ) - 1:
      a     :			Tuple				=   sagrams[i] + ' ' + sagrams[i + 1]
      sagrams.append(A_      )
    if i < len(A_      ) - 2:
      a     :			List[str]				=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
      sagrams.append(A_      )
    if i < len(A_      ) - 3:
      a     :			List[str]				=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
      sagrams.append(A_      )

  for i in range(0	,			len(A_      ) - 1      ):
    if i < len(A_      ) - 1:
      a     :			List[Any]				=   cagrams[i] + ' ' + cagrams[i + 1]
      cagrams.append(A_      )
    if i < len(A_      ) - 2:
      a     :			List[str]				=   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
      cagrams.append(A_      )
    if i < len(A_      ) - 3:
      a     :			Tuple				=   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
      cagrams.append(A_      )

  ((a),     (a),     (a))     :			Tuple				=   SARIngram(A_	,			A_	,			A_	,			A_      )
  ((a),     (a),     (a))     :			Tuple				=   SARIngram(A_	,			A_	,			A_	,			A_      )
  ((a),     (a),     (a))     :			List[str]				=   SARIngram(A_	,			A_	,			A_	,			A_      )
  ((a),     (a),     (a))     :			Dict				=   SARIngram(A_	,			A_	,			A_	,			A_      )
  a     :			List[str]				=   sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
  a     :			List[Any]				=   sum([delascore, delascore, delascore, delascore]      ) / 4
  a     :			Union[str, Any]				=   sum([addascore, addascore, addascore, addascore]      ) / 4
  a     :			Any				=   (avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore




def 			snake_case  (A_      :Tuple	,			A_      :bool = True	,			A_      :str = "13a"	,			A_      :bool = True      ):







  '''simple docstring'''

  if lowercase:
    a     :			Tuple				=   sentence.lower()

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

  if not return_str:
    a     :			List[Any]				=   normalized_sent.split()

  return normalized_sent






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





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      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 				lowerCamelCase__      (							self	:					str				,		A	:					Any				,		A	:					str				,		A	:					int			):


      '''simple docstring'''




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












"""simple docstring"""


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       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

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

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      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 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_floataa_inference(expected_max_diff=1E-1			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2				,		)




