


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a_ :List[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'

a_ :Union[str, 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'


a_ :Optional[int]  							=			'\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    a   (							A__							)	-> Any:




      '''simple docstring'''

      def remove_articles(A__							):
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		re.compile(r'''\b(a|an|the)\b'''      ,    re.UNICODE							)
            return re.sub(A__      ,    ''' '''      ,    A__							)

      def white_space_fix(A__							):
            return " ".join(text.split()							)

      def remove_punc(A__							):
            SCREAMING_SNAKE_CASE__       :  Dict						     =		set(string.punctuation							)
            return "".join(ch for ch in text if ch not in exclude							)

      def lower(A__							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(A__							)							)							)							)




def    a   (							A__      ,    A__							)	-> Tuple:




      '''simple docstring'''

      return int(normalize_answer(A__							) == normalize_answer(A__							)							)




def    a   (							A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  int						     =		[any(compute_exact(A__      ,    A__							) for ref in refs							) for pred, refs in zip(A__      ,    A__							)]
      return (sum(A__							) / len(A__							)) * 1_0_0




def    a   (							A__      ,    A__      ,    A__      ,    A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[rgram for rgrams in rgramslist for rgram in rgrams]
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		Counter(A__							)

      SCREAMING_SNAKE_CASE__       :  str						     =		Counter(A__							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		Counter()
      for sgram, scount in sgramcounter.items():
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scount * numref

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		Counter(A__							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		Counter()
      for cgram, ccount in cgramcounter.items():
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		ccount * numref

      # KEEP
      SCREAMING_SNAKE_CASE__       :  int						     =		sgramcounter_rep & cgramcounter_rep
      SCREAMING_SNAKE_CASE__       :  Any						     =		keepgramcounter_rep & rgramcounter
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		sgramcounter_rep & rgramcounter

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

      # DELETION
      SCREAMING_SNAKE_CASE__       :  int						     =		sgramcounter_rep - cgramcounter_rep
      SCREAMING_SNAKE_CASE__       :  Any						     =		delgramcounter_rep - rgramcounter
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		sgramcounter_rep - rgramcounter
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		0
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		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(A__							) > 0:
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		deltmpscorea / len(A__							)

      # ADDITION
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		set(A__							) - set(A__							)
      SCREAMING_SNAKE_CASE__       :  str						     =		set(A__							) & set(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		set(A__							) - set(A__							)

      SCREAMING_SNAKE_CASE__       :  str						     =		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__       :  List[Any]						     =		1
      if len(A__							) > 0:
            SCREAMING_SNAKE_CASE__       :  str						     =		addtmpscore / len(A__							)
      if len(A__							) > 0:
            SCREAMING_SNAKE_CASE__       :  int						     =		addtmpscore / len(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		0
      if addscore_precision > 0 or addscore_recall > 0:
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)




def    a   (							A__      ,    A__      ,    A__							)	-> Tuple:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		len(A__							)

      SCREAMING_SNAKE_CASE__       :  Dict						     =		ssent.split(''' '''							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		csent.split(''' '''							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[]
      SCREAMING_SNAKE_CASE__       :  str						     =		[]
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
      SCREAMING_SNAKE_CASE__       :  Any						     =		[]
      SCREAMING_SNAKE_CASE__       :  int						     =		[]

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

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




def    a   (							A__      ,    A__ = True      ,    A__ = "13a"      ,    A__ = True							)	-> Optional[int]:




      '''simple docstring'''

      if lowercase:
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		sentence.lower()

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

      if not return_str:
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		normalized_sent.split()

      return normalized_sent




def    a   (							A__      ,    A__      ,    A__							)	-> int:




      '''simple docstring'''

      if not (len(A__							) == len(A__							) == len(A__							)):
            raise ValueError('''Sources length must match predictions and references lengths.'''							)
      SCREAMING_SNAKE_CASE__       :  str						     =		0
      for src, pred, refs in zip(A__      ,    A__      ,    A__							):
            sari_score += SARIsent(normalize(A__							)      ,    normalize(A__							)      ,    [normalize(A__							) for sent in refs]							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		sari_score / len(A__							)
      return 1_0_0 * sari_score




def    a   (							A__      ,    A__      ,    A__="exp"      ,    A__=None      ,    A__=False      ,    A__=False      ,    A__=False      ,    )	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		len(references[0]							)
      if any(len(A__							) != references_per_prediction for refs in references							):
            raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		[[refs[i] for refs in references] for i in range(A__							)]
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		sacrebleu.corpus_bleu(
          A__      ,    A__      ,    smooth_method=A__      ,    smooth_value=A__      ,    force=A__      ,    lowercase=A__      ,    use_effective_order=A__      ,    )
      return output.score





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







     def 							lowercase__       (						self :						List[Any]      ):
           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 :						Optional[Any]   ,   _lowercase :						Union[str, Any]   ,   _lowercase :						Dict   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{}
           result.update({'''sari''': compute_sari(sources=_lowercase   ,   predictions=_lowercase   ,   references=_lowercase      )}      )
           result.update({'''sacrebleu''': compute_sacrebleu(predictions=_lowercase   ,   references=_lowercase      )}      )
           result.update({'''exact''': compute_em(predictions=_lowercase   ,   references=_lowercase      )}      )
           return result












"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

