






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_UpperCAmelCase				:	Dict    						=    '\\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'

_UpperCAmelCase				:	List[str]    						=    '\\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'

_UpperCAmelCase				:	Optional[int]    						=    '\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 		lowerCAmelCase_							(     datasets.Metric   ):





     def 	lowerCamelCase       (  self     )		->							int:
        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       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = "warn"       ,   )		->							Optional[Any]:
        if scheme is not None:
           try:
              lowerCamelCase											=importlib.import_module("""seqeval.scheme"""     )
              lowerCamelCase											=getattr(_UpperCAmelCase       ,   _UpperCAmelCase     )
           except AttributeError:
              raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''     )
        lowerCamelCase											=classification_report(
            y_true=_UpperCAmelCase       ,   y_pred=_UpperCAmelCase       ,   suffix=_UpperCAmelCase       ,   output_dict=_UpperCAmelCase       ,   scheme=_UpperCAmelCase       ,   mode=_UpperCAmelCase       ,   sample_weight=_UpperCAmelCase       ,   zero_division=_UpperCAmelCase       ,   )
        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=_UpperCAmelCase       ,   y_pred=_UpperCAmelCase     )

        return scores







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase				:	Any    						=    get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 		lowerCAmelCase_							(     UpperCamelCase							,    unittest.TestCase   ):
     __lowerCamelCase					:						List[Any]     =    ErnieMTokenizer
     __lowerCamelCase					:						Optional[Any]     =    False
     __lowerCamelCase					:						Optional[int]     =    True
     __lowerCamelCase					:						str     =    False
     __lowerCamelCase					:						Any     =    False





     def 	lowerCamelCase       (  self     )		->							Any:
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )
        tokenizer.save_pretrained(self.tmpdirname     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Union[str, Any]:
        lowerCamelCase											="""this is a test"""
        lowerCamelCase											="""this is a test"""
        return input_text, output_text





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											="""<pad>"""
        lowerCamelCase											=0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase     )       ,   _UpperCAmelCase     )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase     )       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=list(self.get_tokenizer().get_vocab().keys()     )

        self.assertEqual(vocab_keys[0]       ,   """<pad>"""     )
        self.assertEqual(vocab_keys[1]       ,   """<unk>"""     )
        self.assertEqual(vocab_keys[-1]       ,   """▁eloquent"""     )
        self.assertEqual(len(_UpperCAmelCase     )       ,   30000     )





     def 	lowerCamelCase       (  self     )		->							Any:
        self.assertEqual(self.get_tokenizer().vocab_size       ,   30000     )





     def 	lowerCamelCase       (  self     )		->							Any:
        if not self.test_rust_tokenizer:
           return

        lowerCamelCase											=self.get_tokenizer()
        lowerCamelCase											=self.get_rust_tokenizer()

        lowerCamelCase											="""I was born in 92000, and this is falsé."""

        lowerCamelCase											=tokenizer.tokenize(_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.tokenize(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )

        lowerCamelCase											=tokenizer.encode(_UpperCAmelCase       ,   add_special_tokens=_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.encode(_UpperCAmelCase       ,   add_special_tokens=_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )

        lowerCamelCase											=self.get_rust_tokenizer()
        lowerCamelCase											=tokenizer.encode(_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.encode(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   do_lower_case=_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )

        lowerCamelCase											=tokenizer.tokenize("""This is a test"""     )
        self.assertListEqual(_UpperCAmelCase       ,   ["""▁this""", """▁is""", """▁a""", """▁test"""]     )

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )       ,   [48, 25, 21, 1289]     )

        lowerCamelCase											=tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            _UpperCAmelCase       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]     )
        lowerCamelCase											=tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]     )

        lowerCamelCase											=tokenizer.convert_ids_to_tokens(_UpperCAmelCase     )
        self.assertListEqual(
            _UpperCAmelCase       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]       ,   )





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )

        lowerCamelCase											=tokenizer.encode("""sequence builders"""     )
        lowerCamelCase											=tokenizer.encode("""multi-sequence build"""     )

        lowerCamelCase											=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
        lowerCamelCase											=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       ,   _UpperCAmelCase     )

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]





     @slow
     def 	lowerCamelCase       (  self     )		->							Dict:
        # fmt: off
        lowerCamelCase											={"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_UpperCAmelCase       ,   model_name="""susnato/ernie-m-base_pytorch"""       ,   sequences=[
                """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
                """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
                """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
                """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
                """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
                """conditioning on both left and right context in all layers.""",
                """The quick brown fox jumps over the lazy dog.""",
            ]       ,   )