




'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowerCAmelCase_    	=							'''\
@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_    	=							'''\
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_    	=							'''
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   _snake_case( datasets.Metric			):






 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Optional[int]:






    """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 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     Tuple						,		a					:     str						,		a					:     bool = False						,		a					:     Optional[str] = None						,		a					:     Optional[str] = None						,		a					:     Optional[List[int]] = None						,		a					:     Union[str, int] = "warn"						,		)					->      Optional[int]:






    """simple docstring"""


    if scheme is not None:
       try:
          A__         =    importlib.import_module('seqeval.scheme'						)
          A__         =    getattr(a						,		a						)
       except AttributeError:
          raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""						)
    A__         =    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'						)
    A__         =    report.pop('micro avg'						)

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

    return scores








'''simple docstring'''







from dataclasses import dataclass

lowerCAmelCase_    	=							'''0.1'''
lowerCAmelCase_    	=							'''Lucia Harcekova'''

@dataclass
class   _snake_case:
 __snake_case:       int
 __snake_case:       int
 __snake_case:       str






 def __repr__(self					:     Optional[int]						)					->      str:






    """simple docstring"""


    return f"""({self.offset}, {self.length}, {self.indicator})"""




class   _snake_case:






 def __init__(self					:     Optional[Any]						,		a					:     int = 13						,		a					:     int = 6						)					->      None:






    """simple docstring"""


    A__         =    window_size
    A__         =    lookahead_buffer_size
    A__         =    self.window_size - self.lookahead_buffer_size






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     str						)					->      list[Token]:






    """simple docstring"""


    A__         =    []
    A__         =    ''

    # while there are still characters in text to compress
    while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       A__         =    self._find_encoding_token(a						,		a						)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(a						) > self.search_buffer_size:
          A__         =    search_buffer[-self.search_buffer_size :]

       # update the text
       A__         =    text[token.length + 1 :]

       # append the token to output
       output.append(a						)

    return output






 def 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     list[Token]						)					->      str:






    """simple docstring"""


    A__         =    ''

    for token in tokens:
       for _ in range(token.length						):
          output += output[-token.offset]
       output += token.indicator

    return output






 def 	_UpperCamelCase   (self					:     Any						,		a					:     str						,		a					:     str						)					->      Token:






    """simple docstring"""


    if not text:
       raise ValueError('We need some text to work with.'						)

    # Initialise result parameters to default values
    A__     , A__         =    0, 0

    if not search_buffer:
       return Token(a						,		a						,		text[length]						)

    for i, character in enumerate(a						):
       A__         =    len(a						) - i
       if character == text[0]:
          A__         =    self._match_length_from_index(a						,		a						,		0						,		a						)
          # if the found length is bigger than the current or if it's equal,
          # which means it's offset is smaller: update offset and length
          if found_length >= length:
             A__     , A__         =    found_offset, found_length

    return Token(a						,		a						,		text[length]						)






 def 	_UpperCamelCase   (self					:     int						,		a					:     str						,		a					:     str						,		a					:     int						,		a					:     int						)					->      int:






    """simple docstring"""


    if not text or text[text_index] != window[window_index]:
       return 0
    return 1 + self._match_length_from_index(
        a						,		window + text[text_index]						,		text_index + 1						,		window_index + 1						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # Initialize compressor class
    lowerCAmelCase_    	=							LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

    # Example
    lowerCAmelCase_    	=							'''cabracadabrarrarrad'''
    lowerCAmelCase_    	=							lzaa_compressor.compress(TEXT)
    print(lzaa_compressor.compress('''ababcbababaa'''))
    lowerCAmelCase_    	=							lzaa_compressor.decompress(compressed_text)
    assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."


