

"""simple docstring"""





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union




class    _lowerCAmelCase    (      enum.Enum  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[Any]  						=      "timm"
 __UpperCAmelCase      :   Optional[int]  						=      "transformers"







def     _A    (    UpperCamelCase_	:			Optional[Iterable[str]],     UpperCamelCase_	:			Optional[Iterable[int]],     UpperCamelCase_	:			Optional[Iterable[str]])      ->		Dict:
   '''simple docstring'''


   if stage_names is None:
      raise ValueError("Stage_names must be set for transformers backbones")

   if out_features is not None:
      if not isinstance(UpperCamelCase_,     (list,)):
         raise ValueError(F"""out_features must be a list {type(UpperCamelCase_)}""")
      if any(feat not in stage_names for feat in out_features):
         raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}""")

   if out_indices is not None:
      if not isinstance(UpperCamelCase_,     (list, tuple)):
         raise ValueError(F"""out_indices must be a list or tuple, got {type(UpperCamelCase_)}""")
      if any(idx >= len(UpperCamelCase_) for idx in out_indices):
         raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}")

   if out_features is not None and out_indices is not None:
      if len(UpperCamelCase_) != len(UpperCamelCase_):
         raise ValueError("out_features and out_indices should have the same length if both are set")
      if out_features != [stage_names[idx] for idx in out_indices]:
         raise ValueError("out_features and out_indices should correspond to the same stages if both are set")




def     _A    (    UpperCamelCase_	:			Optional[List[str]],     UpperCamelCase_	:			Optional[Union[List[int], Tuple[int]]],     UpperCamelCase_	:			List[str],     )      ->		str:
   '''simple docstring'''


   if out_indices is None and out_features is None:
      __lowercase								=		[len(UpperCamelCase_) - 1]
      __lowercase								=		[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      __lowercase								=		[stage_names.index(UpperCamelCase_) for layer in out_features]
   elif out_features is None and out_indices is not None:
      __lowercase								=		[stage_names[idx] for idx in out_indices]
   return out_features, out_indices




def     _A    (    UpperCamelCase_	:			Optional[List[str]],     UpperCamelCase_	:			Optional[Union[List[int], Tuple[int]]],     UpperCamelCase_	:			List[str],     )      ->		Tuple[List[str], List[int]]:
   '''simple docstring'''


   verify_out_features_out_indices(out_features=UpperCamelCase_,     out_indices=UpperCamelCase_,     stage_names=UpperCamelCase_)
   __lowercase						,__lowercase								=		_align_output_features_output_indices(
       out_features=UpperCamelCase_,     out_indices=UpperCamelCase_,     stage_names=UpperCamelCase_)
   # Verify that the aligned out_features and out_indices are valid
   verify_out_features_out_indices(out_features=UpperCamelCase_,     out_indices=UpperCamelCase_,     stage_names=UpperCamelCase_)
   return output_features, output_indices




class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   Optional[BackboneType]  						=      None


 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     Dict      ):
    if getattr(self,   "_backbone",   UpperCAmelCase__      ) is None:
       raise ValueError("self._backbone must be set before calling _init_timm_backbone"      )

    # These will diagree with the defaults for the transformers models e.g. for resnet50
    # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
    # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
    __lowercase								=		[stage["module"] for stage in self._backbone.feature_info.info]
    __lowercase								=		[stage["num_chs"] for stage in self._backbone.feature_info.info]
    __lowercase								=		self._backbone.feature_info.out_indices
    __lowercase								=		self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=self.stage_names      )
    __lowercase						,__lowercase								=		out_features, out_indices


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     Tuple      ):
    __lowercase								=		getattr(UpperCAmelCase__,   "stage_names"      )
    __lowercase								=		getattr(UpperCAmelCase__,   "out_features",   UpperCAmelCase__      )
    __lowercase								=		getattr(UpperCAmelCase__,   "out_indices",   UpperCAmelCase__      )

    __lowercase								=		stage_names
    __lowercase						,__lowercase								=		get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=UpperCAmelCase__      )
    # Number of channels for each stage. This is set in the transformer backbone model init
    __lowercase								=		None


 def    _lowercase					(     self				:     Optional[int],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		config

    __lowercase								=		getattr(UpperCAmelCase__,   "use_timm_backbone",   UpperCAmelCase__      )
    __lowercase								=		BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

    if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(UpperCAmelCase__      )
    elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(UpperCAmelCase__      )
    else:
       raise ValueError(F"""backbone_type {self.backbone_type} not supported."""      )


 @property
 def    _lowercase					(     self				:     Tuple      ):
    return self._out_features


 @out_features.setter
 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     List[str]      ):
    __lowercase						,__lowercase								=		get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=self.stage_names      )


 @property
 def    _lowercase					(     self				:     Optional[int]      ):
    return self._out_indices


 @out_indices.setter
 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Union[Tuple[int], List[int]]      ):
    __lowercase						,__lowercase								=		get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=self.stage_names      )


 @property
 def    _lowercase					(     self				:     int      ):
    # the current backbones will output the number of channels for each stage
    # even if that stage is not in the out_features list.
    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names      )}


 @property
 def    _lowercase					(     self				:     Union[str, Any]      ):
    return [self.out_feature_channels[name] for name in self.out_features]


 def    _lowercase					(     self				:     Union[str, Any],   *UpperCAmelCase__				:     Optional[Any],   **UpperCAmelCase__				:     List[str]      ):
    __lowercase								=		dict(inspect.signature(self.forward      ).parameters      )
    __lowercase								=		{k: v for k, v in kwargs.items() if k in signature}
    return self(*UpperCAmelCase__,   **UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int],   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     Optional[bool] = None,   UpperCAmelCase__				:     Optional[bool] = None,   UpperCAmelCase__				:     Optional[bool] = None,   ):
    raise NotImplementedError("This method should be implemented by the derived class."      )



 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		super().to_dict()
    __lowercase								=		output.pop("_out_features"      )
    __lowercase								=		output.pop("_out_indices"      )
    return output







class    _lowerCAmelCase    :







 """simple docstring"""







 @property
 def    _lowercase					(     self				:     Optional[int]      ):
    return self._out_features


 @out_features.setter
 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     List[str]      ):
    __lowercase						,__lowercase								=		get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=self.stage_names      )


 @property
 def    _lowercase					(     self				:     Any      ):
    return self._out_indices


 @out_indices.setter
 def    _lowercase					(     self				:     int,   UpperCAmelCase__				:     Union[Tuple[int], List[int]]      ):
    __lowercase						,__lowercase								=		get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__,   stage_names=self.stage_names      )



 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		super().to_dict()
    __lowercase								=		output.pop("_out_features"      )
    __lowercase								=		output.pop("_out_indices"      )
    return output







"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_a					 =				'\\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'

_a					 =				'\\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'

_a					 =				'\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  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[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				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		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"      )
    __lowercase								=		report.pop("micro avg"      )

    __lowercase								=		{
        type_name: {
            "precision": score["precision"],
            "recall": score["recall"],
            "f1": score["f1-score"],
            "number": score["support"],
        }
        for type_name, score in report.items()
    }
    __lowercase								=		overall_score["precision"]
    __lowercase								=		overall_score["recall"]
    __lowercase								=		overall_score["f1-score"]
    __lowercase								=		accuracy_score(y_true=UpperCAmelCase__,   y_pred=UpperCAmelCase__      )

    return scores




