






"""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					):



  def __init__(self	:		Optional[int]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		str					,      lowercase_	:		Dict = None					,      lowercase_	:		Optional[Any] = None					,      lowercase_	:		List[Any] = None					,      lowercase_	:		Union[str, Any] = "longest"					,      lowercase_	:		Tuple = True					,      lowercase_	:		Union[str, Any] = 512					,      lowercase_	:		Dict = None					,      lowercase_	:		str = True					,      lowercase_	:		Dict = True					,      lowercase_	:		Any = True					,      **lowercase_	:		Any					,      )		->				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     A_					(cls	:		Optional[Any]					,      lowercase_	:		List[str]					,      **lowercase_	:		Optional[Any] )		->				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 lowercase_ : 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     A_					(cls	:		Optional[int]					,      lowercase_	:		Dict					,      *lowercase_	:		int					,      **lowercase_	:		Tuple )		->				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     A_					(self	:		Tuple					,      lowercase_	:		List[Any] )		->				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     A_					(self	:		List[str]					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]=None					,      lowercase_	:		int=None					,      lowercase_	:		Union[str, Any]=None					,      lowercase_	:		Tuple=None					,      lowercase_	:		Union[str, Any]=None					,      lowercase_	:		List[Any]=None					,      lowercase_	:		Union[str, Any]=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						:							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						:							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						:							List[str]      =						self.paired_trimmer.trim([text, text_pair] )
    __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						:							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						:							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     A_					(self	:		Union[str, Any] )		->				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





