import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowercase							:      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
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

_lowercase							:      List[Any]      							='''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


_lowercase							:      str      							='''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''



def A__  (			lowercase:     Dict					)				->		List[str]:

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

       def white_space_fix(lowercase:     Optional[Any]					):
              return " ".join(text.split()					)

       def remove_punc(lowercase:     Dict					):
              A						:     Optional[Any]         =set(string.punctuation					)
              return "".join(ch for ch in text if ch not in exclude					)

       def lower(lowercase:     Union[str, Any]					):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase					)					)					)					)



def A__  (			lowercase:     Union[str, Any],		lowercase:     List[Any]					)				->		int:
       return int(normalize_answer(lowercase					) == normalize_answer(lowercase					)					)



def A__  (			lowercase:     int,		lowercase:     List[Any]					)				->		Any:
       A						:     str         =[any(compute_exact(lowercase,		lowercase					) for ref in refs					) for pred, refs in zip(lowercase,		lowercase					)]
       return (sum(lowercase					) / len(lowercase					)) * 100



def A__  (			lowercase:     Tuple,		lowercase:     str,		lowercase:     Union[str, Any],		lowercase:     List[Any]					)				->		List[str]:
       A						:     List[Any]         =[rgram for rgrams in rgramslist for rgram in rgrams]
       A						:     Optional[Any]         =Counter(lowercase					)

       A						:     Any         =Counter(lowercase					)
       A						:     int         =Counter()
       for sgram, scount in sgramcounter.items():
              A						:     str         =scount * numref

       A						:     List[Any]         =Counter(lowercase					)
       A						:     Optional[int]         =Counter()
       for cgram, ccount in cgramcounter.items():
              A						:     Dict         =ccount * numref

       # KEEP
       A						:     int         =sgramcounter_rep & cgramcounter_rep
       A						:     str         =keepgramcounter_rep & rgramcounter
       A						:     Union[str, Any]         =sgramcounter_rep & rgramcounter

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

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

       # ADDITION
       A						:     List[Any]         =set(lowercase					) - set(lowercase					)
       A						:     Optional[Any]         =set(lowercase					) & set(lowercase					)
       A						:     int         =set(lowercase					) - set(lowercase					)

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

       return (keepscore, delscore_precision, addscore)



def A__  (			lowercase:     Optional[Any],		lowercase:     str,		lowercase:     Optional[Any]					)				->		Any:
       A						:     int         =len(lowercase					)

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

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

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



def A__  (			lowercase:     Any,		lowercase:     bool = True,		lowercase:     str = "13a",		lowercase:     bool = True					)				->		str:
       # 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:
              A						:     Union[str, Any]         =sentence.lower()

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

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

       return normalized_sent



def A__  (			lowercase:     Union[str, Any],		lowercase:     int,		lowercase:     List[str]					)				->		int:
       if not (len(lowercase					) == len(lowercase					) == len(lowercase					)):
              raise ValueError('Sources length must match predictions and references lengths.'					)
       A						:     int         =0
       for src, pred, refs in zip(lowercase,		lowercase,		lowercase					):
              sari_score += SARIsent(normalize(lowercase					),		normalize(lowercase					),		[normalize(lowercase					) for sent in refs]					)
       A						:     int         =sari_score / len(lowercase					)
       return 100 * sari_score



def A__  (			lowercase:     Tuple,		lowercase:     Any,		lowercase:     List[Any]="exp",		lowercase:     Tuple=None,		lowercase:     Any=False,		lowercase:     Tuple=False,		lowercase:     List[str]=False,		)				->		str:
       A						:     str         =len(references[0]					)
       if any(len(lowercase					) != references_per_prediction for refs in references					):
              raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
       A						:     Tuple         =[[refs[i] for refs in references] for i in range(lowercase					)]
       A						:     Tuple         =sacrebleu.corpus_bleu(
           lowercase,		lowercase,		smooth_method=lowercase,		smooth_value=lowercase,		force=lowercase,		lowercase=lowercase,		use_effective_order=lowercase,		)
       return output.score



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


  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Any:
         A						:     int         ={}
         result.update({'sari': compute_sari(sources=SCREAMING_SNAKE_CASE__      ,	predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         result.update({'sacrebleu': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         result.update({'exact': compute_em(predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         return result







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






