




import re
import string

import numpy as np

import datasets


_lowerCamelCase      :  Optional[Any]      						=						'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

_lowerCamelCase      :  int      						=						'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

_lowerCamelCase      :  List[Any]      						=						'\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,	_KWARGS_DESCRIPTION		)
class        __snake_case  (datasets.Metric		):



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								return datasets.MetricInfo(
								    description=_DESCRIPTION		,	citation=_CITATION		,	inputs_description=_KWARGS_DESCRIPTION		,	features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""string"""		,	id="""sequence"""      ),
								            """references""": datasets.Value("""string"""		,	id="""sequence"""      ),
								        }      )		,	reference_urls=[]		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		str=None		,	_UpperCAmelCase      :		str=False		,	_UpperCAmelCase      :		Optional[Any]=False		,	_UpperCAmelCase      :		Tuple=False		,	)  ->    str:





								'''simple docstring'''




								if regexes_to_ignore is not None:
												for s in regexes_to_ignore:
																_lowerCAmelCase    :    Dict										=      np.array([re.sub(lowercase__		,	""""""		,	lowercase__      ) for x in predictions]      )
																_lowerCAmelCase    :    Optional[int]										=      np.array([re.sub(lowercase__		,	""""""		,	lowercase__      ) for x in references]      )
								else:
												_lowerCAmelCase    :    Tuple										=      np.asarray(lowercase__      )
												_lowerCAmelCase    :    Dict										=      np.asarray(lowercase__      )

								if ignore_case:
												_lowerCAmelCase    :    Dict										=      np.char.lower(lowercase__      )
												_lowerCAmelCase    :    Dict										=      np.char.lower(lowercase__      )

								if ignore_punctuation:
												_lowerCAmelCase    :    Optional[Any]										=      string.punctuation.maketrans(""""""		,	""""""		,	string.punctuation      )
												_lowerCAmelCase    :    int										=      np.char.translate(lowercase__		,	table=lowercase__      )
												_lowerCAmelCase    :    Tuple										=      np.char.translate(lowercase__		,	table=lowercase__      )

								if ignore_numbers:
												_lowerCAmelCase    :    Any										=      string.digits.maketrans(""""""		,	""""""		,	string.digits      )
												_lowerCAmelCase    :    Optional[Any]										=      np.char.translate(lowercase__		,	table=lowercase__      )
												_lowerCAmelCase    :    str										=      np.char.translate(lowercase__		,	table=lowercase__      )

								_lowerCAmelCase    :    str										=      predictions == references

								return {"exact_match": np.mean(lowercase__      ) * 100}







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      [int(UpperCamelCase_       ) for i in ip_va_address.split("""."""       ) if i.isdigit()]
				return len(UpperCamelCase_       ) == 4 and all(0 <= int(UpperCamelCase_       ) <= 254 for octet in octets       )


if __name__ == "__main__":
	_lowerCamelCase      :  List[str]      						=						input().strip()
	_lowerCamelCase      :  int      						=						"valid" if is_ip_va_address_valid(ip) else "invalid"
	print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')

