





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


A_        =  "\\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_        =  "\\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_        =  "\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 SCREAMING_SNAKE_CASE_			(       datasets.Metric ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = False						,	_lowerCAmelCase = None						,	_lowerCAmelCase = None						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "warn"						,	):
										if scheme is not None:
															try:
																				lowerCamelCase__      =							importlib.import_module("seqeval.scheme"						)
																				lowerCamelCase__      =							getattr(_lowerCAmelCase						,	_lowerCAmelCase						)
															except AttributeError:
																				raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"						)
										lowerCamelCase__      =							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"						)
										lowerCamelCase__      =							report.pop("micro avg"						)

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

										return scores








# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

