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

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

        return scores
"""simple docstring"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		requests.get(UpperCAmelCase__		,      stream=UpperCAmelCase__      )

						with open(f'{path}/{img_name}'		,      """wb+"""      ) as img_file:
												shutil.copyfileobj(response.raw		,      UpperCAmelCase__      )
						del response
						return apod_data


def 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


if __name__ == "__main__":
						print(save_apod("""YOUR API KEY"""))
						UpperCamelCase_							:							Optional[Any]				 =		get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
						print(apollo_aaaa_items[0]["""data"""][0]["""description"""])

