





'''simple docstring'''






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

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


A__				:   Any      =logging.getLogger(__name__)




class  UpperCAmelCase     (			A_   ):




      def __init__(			self			: Optional[int]			,			__snake_case			: Tuple=-1  )				->      int:
            _lowerCAmelCase							     =							label_idx




      def 				lowercase__    (			self			: Any			,			__snake_case			: int			,			__snake_case			: Union[Split, str]  )				->      List[InputExample]:



            if isinstance(_snake_case			,			_snake_case  ):
                  _lowerCAmelCase							     =							mode.value
            _lowerCAmelCase							     =							os.path.join(_snake_case			,			f"{mode}.txt"  )
            _lowerCAmelCase							     =							1
            _lowerCAmelCase							     =							[]
            with open(_snake_case			,			encoding="""utf-8"""  ) as f:
                  _lowerCAmelCase							     =							[]
                  _lowerCAmelCase							     =							[]
                  for line in f:
                        if line.startswith("""-DOCSTART-"""  ) or line == "" or line == "\n":
                              if words:
                                    examples.append(InputExample(guid=f"{mode}-{guid_index}"			,			words=_snake_case			,			labels=_snake_case  )  )
                                    guid_index += 1
                                    _lowerCAmelCase							     =							[]
                                    _lowerCAmelCase							     =							[]
                        else:
                              _lowerCAmelCase							     =							line.split(""" """  )
                              words.append(splits[0]  )
                              if len(_snake_case  ) > 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=_snake_case			,			labels=_snake_case  )  )
            return examples




      def 				lowercase__    (			self			: str			,			__snake_case			: TextIO			,			__snake_case			: TextIO			,			__snake_case			: List  )				->      List[Any]:
            _lowerCAmelCase							     =							0
            for line in test_input_reader:
                  if line.startswith("""-DOCSTART-"""  ) or line == "" or line == "\n":
                        writer.write(_snake_case  )
                        if not preds_list[example_id]:
                              example_id += 1
                  elif preds_list[example_id]:
                        _lowerCAmelCase							     =							line.split()[0] + ''' ''' + preds_list[example_id].pop(0  ) + '''\n'''
                        writer.write(_snake_case  )
                  else:
                        logger.warning("""Maximum sequence length exceeded: No prediction for \'%s\'."""			,			line.split()[0]  )




      def 				lowercase__    (			self			: Tuple			,			__snake_case			: str  )				->      List[str]:



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




class  UpperCAmelCase     (			A_   ):




      def __init__(			self			: str  )				->      Any:
            super().__init__(label_idx=-2  )




      def 				lowercase__    (			self			: str			,			__snake_case			: str  )				->      List[str]:



            if path:
                  with open(_snake_case			,			"""r"""  ) as f:
                        _lowerCAmelCase							     =							f.read().splitlines()
                  if "O" not in labels:
                        _lowerCAmelCase							     =							['''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  UpperCAmelCase     (			A_   ):




      def 				lowercase__    (			self			: int			,			__snake_case			: Optional[int]			,			__snake_case			: Union[Split, str]  )				->      List[InputExample]:



            if isinstance(_snake_case			,			_snake_case  ):
                  _lowerCAmelCase							     =							mode.value
            _lowerCAmelCase							     =							os.path.join(_snake_case			,			f"{mode}.txt"  )
            _lowerCAmelCase							     =							1
            _lowerCAmelCase							     =							[]

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




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: TextIO			,			__snake_case			: TextIO			,			__snake_case			: List  )				->      str:
            _lowerCAmelCase							     =							0
            for sentence in parse_incr(_snake_case  ):
                  _lowerCAmelCase							     =							preds_list[example_id]
                  _lowerCAmelCase							     =							''''''
                  for token in sentence:
                        out += f"{token['form']} ({token['upos']}|{s_p.pop(0  )}) "
                  out += "\n"
                  writer.write(_snake_case  )
                  example_id += 1




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: str  )				->      List[str]:



            if path:
                  with open(_snake_case			,			"""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",
                  ]











"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\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'

lowerCAmelCase_   						=   '\\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'

lowerCAmelCase_   						=   '\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    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""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 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""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 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

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