






"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase					=     """\
@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},
}
"""

UpperCamelCase					=     """\
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.
"""

UpperCamelCase					=     """
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 						_a  (		datasets.Metric       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   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 __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = False					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = "warn"					, )			->   List[Any]:




          '''simple docstring'''




          if scheme is not None:
                 try:
                        lowercase__:							str         =     importlib.import_module("seqeval.scheme")
                        lowercase__:							Union[str, Any]         =     getattr(UpperCAmelCase_					, UpperCAmelCase_)
                 except AttributeError:
                        raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}""")
          lowercase__:							List[Any]         =     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__:							List[Any]         =     report.pop("micro avg")

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

          return scores














'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )