


"""simple docstring"""






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class 	A_       (     tf.keras.layers.Layer   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "longest"  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = 5_12  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					**__UpperCAmelCase  ,					)				->					Union[str, Any]:
     super().__init__()
     if use_fast_bert_tokenizer:
        a   :	int						=    FastBertTokenizer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case_nfd_strip_accents=__UpperCAmelCase  ,					**__UpperCAmelCase						)
     else:
        a   :	Optional[Any]						=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=__UpperCAmelCase  ,					key_dtype=tf.string  ,					values=tf.range(tf.size(__UpperCAmelCase  ,					out_type=tf.intaa						)  ,					dtype=tf.intaa						)  ,					value_dtype=tf.intaa  ,					)  ,					num_oov_buckets=1  ,					)
        a   :	str						=    BertTokenizerLayer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case=__UpperCAmelCase  ,					**__UpperCAmelCase						)

     a   :	str						=    vocab_list
     a   :	Dict						=    do_lower_case
     a   :	Dict						=    cls_token_id or vocab_list.index('[CLS]'						)
     a   :	Tuple						=    sep_token_id or vocab_list.index('[SEP]'						)
     a   :	Optional[Any]						=    pad_token_id or vocab_list.index('[PAD]'						)
     a   :	Tuple						=    ShrinkLongestTrimmer(max_length - 3  ,					axis=1						)  # Allow room for special tokens
     a   :	Union[str, Any]						=    max_length
     a   :	Tuple						=    padding
     a   :	List[Any]						=    truncation
     a   :	Union[str, Any]						=    pad_to_multiple_of
     a   :	List[Any]						=    return_token_type_ids
     a   :	Optional[int]						=    return_attention_mask
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:  # noqa: F821
     a   :	List[Any]						=    kwargs.pop('do_lower_case'  ,					__UpperCAmelCase						)
     a   :	str						=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     a   :	List[Any]						=    kwargs.pop('cls_token_id'  ,					__UpperCAmelCase						)
     a   :	int						=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     a   :	Tuple						=    kwargs.pop('sep_token_id'  ,					__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     a   :	List[Any]						=    kwargs.pop('pad_token_id'  ,					__UpperCAmelCase						)
     a   :	Any						=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     a   :	Optional[Any]						=    tokenizer.get_vocab()
     a   :	Optional[Any]						=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  ,					key=lambda __UpperCAmelCase						: x[1]						)
     a   :	Optional[Any]						=    [entry[0] for entry in vocab]
     return cls(
         vocab_list=__UpperCAmelCase  ,					do_lower_case=__UpperCAmelCase  ,					cls_token_id=__UpperCAmelCase  ,					sep_token_id=__UpperCAmelCase  ,					pad_token_id=__UpperCAmelCase  ,					**__UpperCAmelCase  ,					)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:
     try:
        a   :	Dict						=    BertTokenizer.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        a   :	Tuple						=    BertTokenizerFast.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     return cls.from_tokenizer(__UpperCAmelCase  ,					**__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     if self.do_lower_case:
        a   :	int						=    case_fold_utfa(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    self.tf_tokenizer.tokenize(__UpperCAmelCase						)
     return tokens.merge_dims(1  ,					-1						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					)				->					Optional[int]:
     if padding is None:
        a   :	List[Any]						=    self.padding
     if padding not in ("longest", "max_length"):
        raise ValueError('Padding must be either \'longest\' or \'max_length\'!'						)
     if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'						)
     if max_length is None:
        a   :	Any						=    self.max_length
     if truncation is None:
        a   :	Any						=    self.truncation
     if pad_to_multiple_of is None:
        a   :	Optional[int]						=    self.pad_to_multiple_of
     if return_token_type_ids is None:
        a   :	str						=    self.return_token_type_ids
     if return_attention_mask is None:
        a   :	Dict						=    self.return_attention_mask
     if not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	List[str]						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None and not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	Any						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None:
        if text.shape.rank > 1:
           raise ValueError('text argument should not be multidimensional when a text pair is supplied!'						)
        if text_pair.shape.rank > 1:
           raise ValueError('text_pair should not be multidimensional!'						)
     if text.shape.rank == 2:
        a	, a   :	Union[str, Any]						=    text[:, 0], text[:, 1]
     a   :	Union[str, Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
     if text_pair is None:  # Unpaired text
        if truncation:
           a   :	List[Any]						=    text[:, : max_length - 2]  # Allow room for special tokens
        a	, a   :	Dict						=    combine_segments(
            (text,)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     else:  # Paired text
        a   :	Optional[Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
        if truncation:
           a	, a   :	List[str]						=    self.paired_trimmer.trim([text, text_pair]						)
        a	, a   :	List[Any]						=    combine_segments(
            (text, text_pair)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     if padding == "longest":
        a   :	List[Any]						=    input_ids.bounding_shape(axis=1						)
        if pad_to_multiple_of is not None:
           # No ceiling division in tensorflow, so we negate floordiv instead
           a   :	Optional[int]						=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length  ,					__UpperCAmelCase						))
     else:
        a   :	Any						=    max_length

     a	, a   :	Any						=    pad_model_inputs(__UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
     a   :	Optional[Any]						=    {'input_ids': input_ids}
     if return_attention_mask:
        a   :	Tuple						=    attention_mask
     if return_token_type_ids:
        a	, a   :	int						=    pad_model_inputs(
            __UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
        a   :	Union[str, Any]						=    token_type_ids
     return output






  def 							lowercase_     (						self						)				->					List[str]:
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }









"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\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"

SCREAMING_SNAKE_CASE__				:  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"

SCREAMING_SNAKE_CASE__				:  Any						= "\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   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     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  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    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'						)
     a   :	Optional[Any]						=    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   :	int						=    overall_score['precision']
     a   :	int						=    overall_score['recall']
     a   :	int						=    overall_score['f1-score']
     a   :	List[str]						=    accuracy_score(y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase						)

     return scores





