
import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowerCamelCase :       Any     					=    '''\
@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},
}
'''

_lowerCamelCase :       Any     					=    '''\
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.
'''

_lowerCamelCase :       Optional[int]     					=    '''
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      SCREAMING_SNAKE_CASE__  (			datasets.Metric      ):





	'''simple docstring'''



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				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			:					Dict    ,						lowercase			:					Tuple    ,						lowercase			:					Optional[Any]    ,						lowercase			:					bool = False    ,						lowercase			:					Optional[str] = None    ,						lowercase			:					Optional[str] = None    ,						lowercase			:					Optional[List[int]] = None    ,						lowercase			:					Union[str, int] = "warn"    ,						):


				'''simple docstring'''


				if scheme is not None:
							try:
										_snake_case						=    importlib.import_module('seqeval.scheme'			)
										_snake_case						=    getattr(lowercase    ,						lowercase			)
							except AttributeError:
										raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''			)
				_snake_case						=    classification_report(
				    y_true=lowercase    ,						y_pred=lowercase    ,						suffix=lowercase    ,						output_dict=lowercase    ,						scheme=lowercase    ,						mode=lowercase    ,						sample_weight=lowercase    ,						zero_division=lowercase    ,						)
				report.pop('macro avg'			)
				report.pop('weighted avg'			)
				_snake_case						=    report.pop('micro avg'			)

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

				return scores

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)