





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__A    :   List[str]          =       "\\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"

__A    :   Optional[Any]          =       "\\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"

__A    :   List[str]          =       "\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      A_	(datasets.Metric      ):
	def 					_lowercase    (   self		):






				'''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 					_lowercase    (   self							, _A							, _A							, _A = False							, _A = None							, _A = None							, _A = None							, _A = "warn"							, ):






				'''simple docstring'''







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

				UpperCAmelCase       		=    {
				    type_name: {
				        '''precision''': score['''precision'''],
				        '''recall''': score['''recall'''],
				        '''f1''': score['''f1-score'''],
				        '''number''': score['''support'''],
				    }
				    for type_name, score in report.items()
				}
				UpperCAmelCase       		=    overall_score['''precision''']
				UpperCAmelCase       		=    overall_score['''recall''']
				UpperCAmelCase       		=    overall_score['''f1-score''']
				UpperCAmelCase       		=    accuracy_score(y_true=_A							, y_pred=_A		)

				return scores










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''model_details''': {
				        '''Organization''': '''testing''',
				        '''Model date''': '''today''',
				        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
				        '''Architecture''': '''Convolutional Neural Network.''',
				    },
				    '''metrics''': '''BLEU and ROUGE-1''',
				    '''evaluation_data''': {
				        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
				        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
				    },
				    '''training_data''': {
				        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
				        '''Preprocessing''': (
				            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
				            ''' https://arxiv.org/pdf/1810.03993.pdf'''
				        ),
				    },
				    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase       		=    os.path.join(_A							, '''modelcard.json'''		)
							model_card_first.to_json_file(_A		)
							UpperCAmelCase       		=    ModelCard.from_json_file(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)



