import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

UpperCAmelCase							   =      """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCAmelCase							   =      """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,      _KWARGS_DESCRIPTION	)
class 							_SCREAMING_SNAKE_CASE(   datasets.Metric	):
			def _lowercase(    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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = "warn"							,     )	->List[str]:
								if scheme is not None:
													try:
																		a  :    Any				=						importlib.import_module('''seqeval.scheme'''    )
																		a  :    Optional[Any]				=						getattr(lowerCAmelCase_							,     lowerCAmelCase_    )
													except AttributeError:
																		raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""    )
								a  :    Optional[Any]				=						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'''    )
								a  :    Dict				=						report.pop('''micro avg'''    )

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

								return scores
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]