






"""simple docstring"""





import re
import string

import numpy as np

import datasets


__lowerCamelCase							=  '\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							=  '\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							=  '\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION			)
class       __A		(   datasets.Metric			):

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							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  :      Any     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int=None     ,		__snake_case  :      Dict=False     ,		__snake_case  :      int=False     ,		__snake_case  :      Dict=False     ,		)			->							Optional[int]:
											if regexes_to_ignore is not None:
																	for s in regexes_to_ignore:
																							__magic_name__:	str							=       np.array([re.sub(__snake_case     ,		""""""     ,		__snake_case					) for x in predictions]					)
																							__magic_name__:	Union[str, Any]							=       np.array([re.sub(__snake_case     ,		""""""     ,		__snake_case					) for x in references]					)
											else:
																	__magic_name__:	Tuple							=       np.asarray(__snake_case					)
																	__magic_name__:	str							=       np.asarray(__snake_case					)

											if ignore_case:
																	__magic_name__:	List[Any]							=       np.char.lower(__snake_case					)
																	__magic_name__:	Any							=       np.char.lower(__snake_case					)

											if ignore_punctuation:
																	__magic_name__:	Dict							=       string.punctuation.maketrans(""""""     ,		""""""     ,		string.punctuation					)
																	__magic_name__:	Optional[int]							=       np.char.translate(__snake_case     ,		table=__snake_case					)
																	__magic_name__:	str							=       np.char.translate(__snake_case     ,		table=__snake_case					)

											if ignore_numbers:
																	__magic_name__:	Optional[Any]							=       string.digits.maketrans(""""""     ,		""""""     ,		string.digits					)
																	__magic_name__:	Any							=       np.char.translate(__snake_case     ,		table=__snake_case					)
																	__magic_name__:	Tuple							=       np.char.translate(__snake_case     ,		table=__snake_case					)

											__magic_name__:	List[str]							=       predictions == references

											return {"exact_match": np.mean(__snake_case					) * 1_0_0}








"""simple docstring"""







from random import randint, random

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							int = 5     ,		):
						"""simple docstring"""






						snake_case   :			str						    = [[-1] * number_of_cells]  # Create a highway without any car
						snake_case   :			str						    = 0
						snake_case   :			Any						    = max(lowerCamelCase_     ,		0 )
						while i < number_of_cells:
												snake_case   :			Optional[int]						    = (
												    randint(0     ,		lowerCamelCase_ ) if random_speed else initial_speed
												)  # Place the cars
												i += (
												    randint(1     ,		max_speed * 2 ) if random_frequency else frequency
												)  # Arbitrary number, may need tuning
						return highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Any						    = 0
						snake_case   :			int						    = highway_now[car_index + 1 :]
						for cell in range(len(lowerCamelCase_ ) ):  # May need a better name for this
												if cells[cell] != -1:  # If the cell is not empty then
																		return distance  # we have the distance we wanted
												distance += 1
						# Here if the car is near the end of the highway
						return distance + get_distance(lowerCamelCase_     ,		-1 )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			List[str]						    = len(lowerCamelCase_ )
						# Beforce calculations, the highway is empty
						snake_case   :			Dict						    = [-1] * number_of_cells

						for car_index in range(lowerCamelCase_ ):
												if highway_now[car_index] != -1:
																		# Add 1 to the current speed of the car and cap the speed
																		snake_case   :			Tuple						    = min(highway_now[car_index] + 1     ,		lowerCamelCase_ )
																		# Number of empty cell before the next car
																		snake_case   :			int						    = get_distance(lowerCamelCase_     ,		lowerCamelCase_ ) - 1
																		# We can't have the car causing an accident
																		snake_case   :			str						    = min(next_highway[car_index]     ,		lowerCamelCase_ )
																		if random() < probability:
																								# Randomly, a driver will slow down
																								snake_case   :			Optional[Any]						    = max(next_highway[car_index] - 1     ,		0 )
						return next_highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = len(highway[0] )

						for i in range(lowerCamelCase_ ):
												snake_case   :			Union[str, Any]						    = update(highway[i]     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Optional[int]						    = [-1] * number_of_cells

												for car_index in range(lowerCamelCase_ ):
																		snake_case   :			List[Any]						    = next_speeds_calculated[car_index]
																		if speed != -1:
																								# Change the position based on the speed (with % to create the loop)
																								snake_case   :			Optional[Any]						    = (car_index + speed) % number_of_cells
																								# Commit the change of position
																								snake_case   :			Tuple						    = speed
												highway.append(lowerCamelCase_ )

						return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()



