



'''simple docstring'''





import re
import string

import numpy as np

import datasets


__a          =		"\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"

__a          =		"\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"

__a          =		"\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION	)
class     UpperCAmelCase_       (       datasets.Metric	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Union[str, Any]						):
          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 lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     int ,     snake_case_					:     Tuple ,     snake_case_					:     List[Any]=None ,     snake_case_					:     Any=False ,     snake_case_					:     Optional[int]=False ,     snake_case_					:     str=False ,     ):
          if regexes_to_ignore is not None:
                for s in regexes_to_ignore:
                      snake_case__  :    int    						=      np.array([re.sub(snake_case_ ,     """""" ,     snake_case_						) for x in predictions]						)
                      snake_case__  :    Any    						=      np.array([re.sub(snake_case_ ,     """""" ,     snake_case_						) for x in references]						)
          else:
                snake_case__  :    int    						=      np.asarray(snake_case_						)
                snake_case__  :    Optional[int]    						=      np.asarray(snake_case_						)

          if ignore_case:
                snake_case__  :    Dict    						=      np.char.lower(snake_case_						)
                snake_case__  :    Tuple    						=      np.char.lower(snake_case_						)

          if ignore_punctuation:
                snake_case__  :    str    						=      string.punctuation.maketrans("""""" ,     """""" ,     string.punctuation						)
                snake_case__  :    Tuple    						=      np.char.translate(snake_case_ ,     table=snake_case_						)
                snake_case__  :    Dict    						=      np.char.translate(snake_case_ ,     table=snake_case_						)

          if ignore_numbers:
                snake_case__  :    Any    						=      string.digits.maketrans("""""" ,     """""" ,     string.digits						)
                snake_case__  :    Union[str, Any]    						=      np.char.translate(snake_case_ ,     table=snake_case_						)
                snake_case__  :    Union[str, Any]    						=      np.char.translate(snake_case_ ,     table=snake_case_						)

          snake_case__  :    Tuple    						=      predictions == references

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





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
