


'''simple docstring'''






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowercase							='\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

lowercase							='\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

lowercase							='\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'






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






       def 			lowerCAmelCase		(			self)						->  str:




          '''simple docstring'''
          return datasets.MetricInfo(
              description=_DESCRIPTION  ,							citation=_CITATION  ,							homepage='https://github.com/chakki-works/seqeval'  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                  {
                      'predictions': datasets.Sequence(datasets.Value('string'  ,							id='label')  ,							id='sequence'),
                      'references': datasets.Sequence(datasets.Value('string'  ,							id='label')  ,							id='sequence'),
                  })  ,							codebase_urls=['https://github.com/chakki-works/seqeval']  ,							reference_urls=['https://github.com/chakki-works/seqeval']  ,							)






       def 			lowerCAmelCase		(			self  ,							snake_case  ,							snake_case  ,							snake_case = False  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = "warn"  ,							)						->  List[Any]:




          '''simple docstring'''




          if scheme is not None:
             try:
                _UpperCAmelCase   :   int							    =importlib.import_module('seqeval.scheme')
                _UpperCAmelCase   :   List[str]							    =getattr(lowerCAmelCase__  ,							lowerCAmelCase__)
             except AttributeError:
                raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")
          _UpperCAmelCase   :   Tuple							    =classification_report(
              y_true=lowerCAmelCase__  ,							y_pred=lowerCAmelCase__  ,							suffix=lowerCAmelCase__  ,							output_dict=lowerCAmelCase__  ,							scheme=lowerCAmelCase__  ,							mode=lowerCAmelCase__  ,							sample_weight=lowerCAmelCase__  ,							zero_division=lowerCAmelCase__  ,							)
          report.pop('macro avg')
          report.pop('weighted avg')
          _UpperCAmelCase   :   Any							    =report.pop('micro avg')

          _UpperCAmelCase   :   int							    ={
              type_name: {
                  'precision': score['precision'],
                  'recall': score['recall'],
                  'f1': score['f1-score'],
                  'number': score['support'],
              }
              for type_name, score in report.items()
          }
          _UpperCAmelCase   :   Union[str, Any]							    =overall_score['precision']
          _UpperCAmelCase   :   Dict							    =overall_score['recall']
          _UpperCAmelCase   :   Tuple							    =overall_score['f1-score']
          _UpperCAmelCase   :   Optional[Any]							    =accuracy_score(y_true=lowerCAmelCase__  ,							y_pred=lowerCAmelCase__)

          return scores





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__       :	List[Any]     				={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Optional[Any]     				=['MBart50Tokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Any     				=['MBart50TokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__       :	Optional[int]     				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





