import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


A     :  List[Any]					     =       "\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\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     :  Dict					     =       "\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

A     :  Tuple					     =       "\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"


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







  def    __A     (							self :       str   )  ->	int:
        if version.parse(scb.__version__   ) < version.parse("1.4.12"   ):
              raise ImportWarning(
                  "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"
                  "You can install it with `pip install \"sacrebleu>=1.4.12\"`."   )
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,     citation=_CITATION  ,     homepage="http://www.cs.umd.edu/~snover/tercom/"  ,     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/mjpost/sacreBLEU#ter"]  ,     reference_urls=[
                "https://github.com/jhclark/tercom",
            ]  ,     )







  def    __A     (							self :       str  ,     __magic_name__ :       Any  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       str = False  ,     __magic_name__ :       List[str] = False  ,     __magic_name__ :       Optional[int] = False  ,     __magic_name__ :       List[str] = False  ,     )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  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_          =  [[refs[i] for refs in references] for i in range(__a   )]

        SCREAMING_SNAKE_CASE_          =  TER(
            normalized=__a  ,     no_punct=__a  ,     asian_support=__a  ,     case_sensitive=__a  ,     )
        SCREAMING_SNAKE_CASE_          =  sb_ter.corpus_score(__a  ,     __a   )

        return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A     :  Any					     =       [
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A     :  Dict					     =       [
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2", "rougeL"]				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2"]				)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]				).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]				).fmeasure.mean()
      )


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "rougeLsum"
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      assert score > score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  ["rouge1", "rouge2", "rougeL"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      assert score_sep == score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      SCREAMING_SNAKE_CASE_          =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				) == calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      SCREAMING_SNAKE_CASE_          =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]     ,      newline_sep=__UpperCamelCase				)["rougeLsum"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]				)["rougeLsum"]
      assert new_score > prev_score





def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  Path("examples/seq2seq/test_data/wmt_en_ro"				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(
          data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)     ,      bootstrap_aggregation=__UpperCamelCase				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)


