

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""



  def __init__(	self    :				Optional[Any]	)					-> List[str]:
      super().__init__()

      self.register_buffer("watermark_image"     ,  torch.zeros((62, 62, 4)	)	)
      SCREAMING_SNAKE_CASE__					     =						None







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				List[PIL.Image.Image]     ,  A_    :				Tuple=None	)					-> Optional[int]:
      # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

      SCREAMING_SNAKE_CASE__					     =						images[0].height
      SCREAMING_SNAKE_CASE__					     =						images[0].width

      SCREAMING_SNAKE_CASE__					     =						sample_size or h

      SCREAMING_SNAKE_CASE__					     =						min(h / sample_size     ,  w / sample_size	)
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						(int(h / coef	), int(w / coef	)) if coef < 1 else (h, w)

      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						1024**2, img_w * img_h
      SCREAMING_SNAKE_CASE__					     =						(Sa / Sa) ** 0.5
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						int(K * 62	), img_w - int(14 * K	), img_h - int(14 * K	)

      if self.watermark_image_as_pil is None:
          SCREAMING_SNAKE_CASE__					     =						self.watermark_image.to(torch.uinta	).cpu().numpy()
          SCREAMING_SNAKE_CASE__					     =						Image.fromarray(A_     ,  mode="RGBA"	)
          SCREAMING_SNAKE_CASE__					     =						watermark_image

      SCREAMING_SNAKE_CASE__					     =						self.watermark_image_as_pil.resize(
          (wm_size, wm_size)     ,  PIL_INTERPOLATION["bicubic"]     ,  reducing_gap=A_	)

      for pil_img in images:
          pil_img.paste(A_     ,  box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)     ,  mask=wm_img.split()[-1]	)

      return images





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A__        =			"\\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"

A__        =			"\\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"


A__        =			"\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 							__a       (					__A ,       __A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						scount * numref

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE__					     =						keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep & rgramcounter

    SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						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__					     =						1
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						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__					     =						keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    SCREAMING_SNAKE_CASE__					     =						0
    if keepscore_precision > 0 or keepscore_recall > 0:
        SCREAMING_SNAKE_CASE__					     =						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE__					     =						delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						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__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						deltmpscorea / len(__A	)

    # ADDITION
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) & set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)

    SCREAMING_SNAKE_CASE__					     =						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__					     =						1
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE__					     =						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						len(__A	)

    SCREAMING_SNAKE_CASE__					     =						ssent.split(" "	)
    SCREAMING_SNAKE_CASE__					     =						csent.split(" "	)
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]

    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    for rsent in rsents:
        SCREAMING_SNAKE_CASE__					     =						rsent.split(" "	)
        SCREAMING_SNAKE_CASE__					     =						[]
        SCREAMING_SNAKE_CASE__					     =						[]
        SCREAMING_SNAKE_CASE__					     =						[]
        ragramslist.append(__A	)
        for i in range(0 ,       len(__A	) - 1	):
            if i < len(__A	) - 1:
                SCREAMING_SNAKE_CASE__					     =						ragrams[i] + " " + ragrams[i + 1]
                ragrams.append(__A	)
            if i < len(__A	) - 2:
                SCREAMING_SNAKE_CASE__					     =						ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                ragrams.append(__A	)
            if i < len(__A	) - 3:
                SCREAMING_SNAKE_CASE__					     =						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__					     =						sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(__A	)
        if i < len(__A	) - 2:
            SCREAMING_SNAKE_CASE__					     =						sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(__A	)
        if i < len(__A	) - 3:
            SCREAMING_SNAKE_CASE__					     =						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__					     =						cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(__A	)
        if i < len(__A	) - 2:
            SCREAMING_SNAKE_CASE__					     =						cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(__A	)
        if i < len(__A	) - 3:
            SCREAMING_SNAKE_CASE__					     =						cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(__A	)

    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						sum([delascore, delascore, delascore, delascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						sum([addascore, addascore, addascore, addascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore





def 							__a       (					__A ,       __A = True ,       __A = "13a" ,       __A = 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:
        SCREAMING_SNAKE_CASE__					     =						sentence.lower()

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

    if not return_str:
        SCREAMING_SNAKE_CASE__					     =						normalized_sent.split()

    return normalized_sent







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


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				List[str]	)					-> 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      _UpperCamelCase    (	self    :				Optional[Any]     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				int	)					-> Dict:
      if not (len(A_	) == len(A_	) == len(A_	)):
          raise ValueError("Sources length must match predictions and references lengths."	)
      SCREAMING_SNAKE_CASE__					     =						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__					     =						sari_score / len(A_	)
      return {"sari": 100 * sari_score}


