'''simple docstring'''




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__SCREAMING_SNAKE_CASE :  Dict  =						"""\
@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},
}
"""

__SCREAMING_SNAKE_CASE :  int  =						"""\
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.
"""

__SCREAMING_SNAKE_CASE :  List[Any]  =						"""
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 			lowerCamelCase_			(datasets.Metric					):
      '''simple docstring'''

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

             _UpperCAmelCase  :						Tuple							   =					{
                 type_name: {
                     "precision": score["precision"],
                     "recall": score["recall"],
                     "f1": score["f1-score"],
                     "number": score["support"],
                 }
                 for type_name, score in report.items()
             }
             _UpperCAmelCase  :						List[str]							   =					overall_score["precision"]
             _UpperCAmelCase  :						str							   =					overall_score["recall"]
             _UpperCAmelCase  :						List[str]							   =					overall_score["f1-score"]
             _UpperCAmelCase  :						Optional[Any]							   =					accuracy_score(y_true=A		,      y_pred=A )

             return scores


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       np.ndarray		,      A		:       int ):
             # Check if RHS is negative
             if np.any(tableau[:, -1]		,      where=tableau[:, -1] < 0 ):
                    raise ValueError("RHS must be > 0" )

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

             # Number of artificial variables to be minimised
             _UpperCAmelCase  :						List[str]							   =					len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             _UpperCAmelCase  :						List[Any]							   =					(self.n_art_vars > 0) + 1

             # Number of slack variables added to make inequalities into equalities
             _UpperCAmelCase  :						int							   =					self.n_rows - self.n_stages

             # Objectives for each stage
             _UpperCAmelCase  :						Optional[Any]							   =					["max"]

             # In two stage simplex, first minimise then maximise
             if self.n_art_vars:
                    self.objectives.append("min" )

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             if len(A ) != 3:
                    raise ValueError("Must provide n_vars, n_slack, and n_art_vars" )

             if not all(x >= 0 and isinstance(A		,      A ) for x in args ):
                    raise ValueError("All arguments must be non-negative integers" )

             # decision | slack | artificial
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						int							   =					[]
             for i in range(3 ):
                    for j in range(args[i] ):
                           titles.append(string_starts[i] + str(j + 1 ) )
             titles.append("RHS" )
             return titles




      def   _A			( self		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _UpperCAmelCase  :						List[str]							   =					(objective == "min") - (objective == "max")
             _UpperCAmelCase  :						Optional[Any]							   =					np.argmax(sign * tableau[0, : self.n_vars] )

             # Choice is only valid if below 0 for maximise, and above for minimise
             if sign * self.tableau[0, col_idx] <= 0:
                    _UpperCAmelCase  :						Dict							   =					True
                    return 0, 0

             # Pivot row is chosen as having the lowest quotient when elements of
             # the pivot column divide the right-hand side

             # Slice excluding the objective rows
             _UpperCAmelCase  :						Any							   =					slice(self.n_stages		,      self.n_rows )

             # RHS
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, -1]

             # Elements of pivot column within slice
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, col_idx]

             # Array filled with nans
             _UpperCAmelCase  :						List[Any]							   =					np.full(self.n_rows - self.n_stages		,      np.nan )

             # If element in pivot column is greater than zeron_stages, return
             # quotient or nan otherwise
             _UpperCAmelCase  :						str							   =					np.divide(A		,      A		,      out=A		,      where=divisor > 0 )

             # Arg of minimum quotient excluding the nan values. n_stages is added
             # to compensate for earlier exclusion of objective columns
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						Optional[Any]							   =					piv_row[col_idx]

             # Entry becomes 1
             piv_row *= 1 / piv_val

             # Variable in pivot column becomes basic, ie the only non-zero entry
             for idx, coeff in enumerate(tableau[:, col_idx] ):
                    tableau[idx] += -coeff * piv_row
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

             _UpperCAmelCase  :						Any							   =					1
             self.n_rows -= 1
             _UpperCAmelCase  :						Dict							   =					0
             _UpperCAmelCase  :						List[str]							   =					False
             return tableau




      def   _A			( self		:       Optional[Any] ):
             # Stop simplex algorithm from cycling.
             for _ in range(100 ):
                    # Completion of each stage removes an objective. If both stages
                    # are complete, then no objectives are left
                    if not self.objectives:
                           _UpperCAmelCase  :						Any							   =					self.generate_col_titles(
                               self.n_vars		,      self.n_slack		,      self.n_art_vars )

                           # Find the values of each variable at optimal solution
                           return self.interpret_tableau(self.tableau		,      self.col_titles )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					self.find_pivot(self.tableau )

                    # If there are no more negative values in objective row
                    if self.stop_iter:
                           # Delete artificial variable columns and rows. Update attributes
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

             for i in range(self.n_vars ):
                    # Gives ids of nonzero entries in the ith column
                    _UpperCAmelCase  :						Dict							   =					np.nonzero(tableau[:, i] )
                    _UpperCAmelCase  :						Dict							   =					len(nonzero[0] )

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _UpperCAmelCase  :						Optional[Any]							   =					tableau[nonzero_rowidx, -1]
                           _UpperCAmelCase  :						Any							   =					rhs_val

        # Check for basic variables
             for title in col_titles:
                    # Don't add RHS or slack variables to output dict
                    if title[0] not in "R-s-a":
                           output_dict.setdefault(A		,      0 )
             return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()

