

from collections import defaultdict







class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    total  # total no of tasks (N)

											# DP table will have a dimension of (2^M)*N
											# initially all values are set to -1
											snake_case_							=    [
											    [-1 for i in range(total + 1       )] for j in range(2 ** len(__a       )       )
											]

											snake_case_							=    defaultdict(__a       )  # stores the list of persons for each task

											# final_mask is used to check if all persons are included by setting all bits
											# to 1
											snake_case_							=    (1 << len(__a       )) - 1




				def     a							(					self      ,   snake_case      ,   snake_case       ):





											if mask == self.final_mask:
																		return 1

											# if not everyone gets the task and no more tasks are available, return 0
											if task_no > self.total_tasks:
																		return 0

											# if case already considered
											if self.dp[mask][task_no] != -1:
																		return self.dp[mask][task_no]

											# Number of ways when we don't this task in the arrangement
											snake_case_							=    self.count_ways_until(__a      ,   task_no + 1       )

											# now assign the tasks one by one to all possible persons and recursively
											# assign for the remaining tasks.
											if task_no in self.task:
																		for p in self.task[task_no]:
																									# if p is already given a task
																									if mask & (1 << p):
																																continue

																									# assign this task to p and change the mask value. And recursively
																									# assign tasks with the new mask value.
																									total_ways_util += self.count_ways_until(mask | (1 << p)      ,   task_no + 1       )

        # save the value.
											snake_case_							=    total_ways_util

											return self.dp[mask][task_no]




				def     a							(					self      ,   snake_case       ):





											for i in range(len(__a       )       ):
																		for j in task_performed[i]:
																									self.task[j].append(__a       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
											return self.count_ways_until(0      ,   1       )


if __name__ == "__main__":
					_UpperCAmelCase		:   List[Any]										=       5  # total no of tasks (the value of N)

					# the list of tasks that can be done by M persons.
					_UpperCAmelCase		:   int										=       [[1, 3, 4], [1, 2, 5], [3, 4]]
					print(
					    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
					        task_performed
					    )
					)








'''simple docstring'''



import re
import string

import numpy as np

import datasets


__lowercase	:						Tuple       =	'\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'

__lowercase	:						List[str]       =	'\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'

__lowercase	:						Any       =	'\n'





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




	def       __UpperCAmelCase							(   self							):

						'''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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=None						,				__a=False						,				__a=False						,				__a=False						,				):

						'''simple docstring'''





						if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
																__a			:       Tuple					    =  np.array([re.sub(__a						,				''						,				__a							) for x in predictions]							)
																__a			:       List[Any]					    =  np.array([re.sub(__a						,				''						,				__a							) for x in references]							)
						else:
											__a			:       int					    =  np.asarray(__a							)
											__a			:       str					    =  np.asarray(__a							)

						if ignore_case:
											__a			:       Dict					    =  np.char.lower(__a							)
											__a			:       List[str]					    =  np.char.lower(__a							)

						if ignore_punctuation:
											__a			:       Dict					    =  string.punctuation.maketrans(''						,				''						,				string.punctuation							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Dict					    =  np.char.translate(__a						,				table=__a							)

						if ignore_numbers:
											__a			:       Optional[int]					    =  string.digits.maketrans(''						,				''						,				string.digits							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Optional[int]					    =  np.char.translate(__a						,				table=__a							)

						__a			:       Any					    =  predictions == references

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



