code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  lowercase__			=	'''\
  @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},
  }
  '''
  
  lowercase__			=	'''\
  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.
  '''
  
  lowercase__			=	'''
  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 						lowercase     (   datasets.Metric):
  
  
  
  
  
  
        def    A__(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    A__(self ,  a__ ,  a__ ,  a__ = False ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = "warn" ,  )       ->   Union[str, Any]:
           if scheme is not None:
              try:
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= importlib.import_module('''seqeval.scheme'''					)
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= getattr(a__ ,  a__					)
              except AttributeError:
                 raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= classification_report(
               y_true=a__ ,  y_pred=a__ ,  suffix=a__ ,  output_dict=a__ ,  scheme=a__ ,  mode=a__ ,  sample_weight=a__ ,  zero_division=a__ ,  )
           report.pop('''macro avg'''					)
           report.pop('''weighted avg'''					)
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= report.pop('''micro avg'''					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
               type_name: {
                   '''precision''': score['''precision'''],
                   '''recall''': score['''recall'''],
                   '''f1''': score['''f1-score'''],
                   '''number''': score['''support'''],
               }
               for type_name, score in report.items()
           }
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= overall_score['''precision''']
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= overall_score['''recall''']
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= overall_score['''f1-score''']
           SCREAMING_SNAKE_CASE__			:				Tuple       						= accuracy_score(y_true=a__ ,  y_pred=a__					)
  
           return scores
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  # rely on isort to merge the imports
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  lowercase__			=	{
      '''configuration_convnextv2''': [
          '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''ConvNextV2Config''',
      ]
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowercase__			=	[
             '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''ConvNextV2ForImageClassification''',
             '''ConvNextV2Model''',
             '''ConvNextV2PreTrainedModel''',
             '''ConvNextV2Backbone''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_convnextva import (
             CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             ConvNextVaConfig,
         )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_convnextva import (
                    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    ConvNextVaBackbone,
                    ConvNextVaForImageClassification,
                    ConvNextVaModel,
                    ConvNextVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  