




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase__				:      Tuple	   = logging.getLogger(__name__)




class       a__	( UpperCamelCase__				):







     def __init__(     self  ,		A=-1			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				label_idx







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   List[InputExample]:



          '''simple docstring'''



          if isinstance(A  ,		A			):
               a     =				mode.value
          a     =				os.path.join(A  ,		F'''{mode}.txt'''			)
          a     =				1
          a     =				[]
          with open(A  ,		encoding="utf-8"			) as f:
               a     =				[]
               a     =				[]
               for line in f:
                    if line.startswith("-DOCSTART-"			) or line == "" or line == "\n":
                         if words:
                              examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,		words=A  ,		labels=A			)			)
                              guid_index += 1
                              a     =				[]
                              a     =				[]
                    else:
                         a     =				line.split(" "			)
                         words.append(splits[0]			)
                         if len(A			) > 1:
                              labels.append(splits[self.label_idx].replace("\n"  ,		""			)			)
                         else:
                              # Examples could have no label for mode = "test"
                              labels.append("O"			)
               if words:
                    examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,		words=A  ,		labels=A			)			)
          return examples







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				0
          for line in test_input_reader:
               if line.startswith("-DOCSTART-"			) or line == "" or line == "\n":
                    writer.write(A			)
                    if not preds_list[example_id]:
                         example_id += 1
               elif preds_list[example_id]:
                    a     =				line.split()[0] + " " + preds_list[example_id].pop(0			) + "\n"
                    writer.write(A			)
               else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."  ,		line.split()[0]			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if path:
               with open(A  ,		"r"			) as f:
                    a     =				f.read().splitlines()
               if "O" not in labels:
                    a     =				["O"] + labels
               return labels
          else:
               return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class       a__	( UpperCamelCase__				):







     def __init__(     self			)  ->   Optional[int]:



          '''simple docstring'''



          super().__init__(label_idx=-2			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if path:
               with open(A  ,		"r"			) as f:
                    a     =				f.read().splitlines()
               if "O" not in labels:
                    a     =				["O"] + labels
               return labels
          else:
               return [
                   "O",
                   "B-ADVP",
                   "B-INTJ",
                   "B-LST",
                   "B-PRT",
                   "B-NP",
                   "B-SBAR",
                   "B-VP",
                   "B-ADJP",
                   "B-CONJP",
                   "B-PP",
                   "I-ADVP",
                   "I-INTJ",
                   "I-LST",
                   "I-PRT",
                   "I-NP",
                   "I-SBAR",
                   "I-VP",
                   "I-ADJP",
                   "I-CONJP",
                   "I-PP",
               ]







class       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   List[InputExample]:



          '''simple docstring'''



          if isinstance(A  ,		A			):
               a     =				mode.value
          a     =				os.path.join(A  ,		F'''{mode}.txt'''			)
          a     =				1
          a     =				[]

          with open(A  ,		encoding="utf-8"			) as f:
               for sentence in parse_incr(A			):
                    a     =				[]
                    a     =				[]
                    for token in sentence:
                         words.append(token["form"]			)
                         labels.append(token["upos"]			)
                    assert len(A			) == len(A			)
                    if words:
                         examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,		words=A  ,		labels=A			)			)
                         guid_index += 1
          return examples







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				0
          for sentence in parse_incr(A			):
               a     =				preds_list[example_id]
               a     =				""
               for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0			)}) '''
               out += "\n"
               writer.write(A			)
               example_id += 1







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if path:
               with open(A  ,		"r"			) as f:
                    return f.read().splitlines()
          else:
               return [
                   "ADJ",
                   "ADP",
                   "ADV",
                   "AUX",
                   "CCONJ",
                   "DET",
                   "INTJ",
                   "NOUN",
                   "NUM",
                   "PART",
                   "PRON",
                   "PROPN",
                   "PUNCT",
                   "SCONJ",
                   "SYM",
                   "VERB",
                   "X",
               ]







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase__				:      Optional[int]	   = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
   from nltk import word_tokenize


lowercase__				:      int	   = "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase__				:      str	   = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase__				:      Tuple	   = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          return datasets.MetricInfo(
              description=_DESCRIPTION  ,		citation=_CITATION  ,		inputs_description=_KWARGS_DESCRIPTION  ,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"  ,		id="sequence"			),
                      "references": datasets.Value("string"  ,		id="sequence"			),
                  }			)  ,		codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]  ,		reference_urls=[
                  "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
                  "https://en.wikipedia.org/wiki/METEOR",
              ]  ,		)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          import nltk

          nltk.download("wordnet"			)
          if NLTK_VERSION >= version.Version("3.6.5"			):
               nltk.download("punkt"			)
          if NLTK_VERSION >= version.Version("3.6.6"			):
               nltk.download("omw-1.4"			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A=0.9  ,		A=3  ,		A=0.5			)  ->   Tuple:



          '''simple docstring'''



          if NLTK_VERSION >= version.Version("3.6.5"			):
               a     =				[
                   meteor_score.single_meteor_score(
                       word_tokenize(A			)  ,		word_tokenize(A			)  ,		alpha=A  ,		beta=A  ,		gamma=A			)
                   for ref, pred in zip(A  ,		A			)
               ]
          else:
               a     =				[
                   meteor_score.single_meteor_score(A  ,		A  ,		alpha=A  ,		beta=A  ,		gamma=A			)
                   for ref, pred in zip(A  ,		A			)
               ]

          return {"meteor": np.mean(A			)}

