



"""simple docstring"""






from collections import defaultdict

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =total  # total no of tasks (N)

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

								lowerCamelCase__		:		Any		     =defaultdict(lowerCamelCase					)  # 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
								lowerCamelCase__		:		List[Any]		     =(1 << len(lowerCamelCase					)) - 1



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Any					)-> Any:
								# if mask == self.finalmask all persons are distributed tasks, return 1
								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
								lowerCamelCase__		:		Optional[int]		     =self.count_ways_until(lowerCamelCase,  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.
								lowerCamelCase__		:		int		     =total_ways_util

								return self.dp[mask][task_no]




					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Dict					)-> int:
								# Store the list of persons for each task
								for i in range(len(lowerCamelCase					)					):
											for j in task_performed[i]:
														self.task[j].append(lowerCamelCase					)

        # 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__":
		_lowercase			:			Tuple										=						5  # total no of tasks (the value of N)

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









"""simple docstring"""






from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


_lowercase			:			List[str]										=						""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):

		class 	__SCREAMING_SNAKE_CASE     (				tr.AbstractTransform       ):


							'''simple docstring'''





							def __init__(	self							:  List[Any],  lowerCamelCase							:  str = " "					)-> List[str]:
										lowerCamelCase__		:		List[str]		     =sentence_delimiter



							def 		snake_case	(	self							:  Any,  lowerCamelCase							:  str					)-> Optional[Any]:
										return list(lowerCamelCase					)




							def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  List[str]					)-> Tuple:
										lowerCamelCase__		:		Optional[int]		     =[]
										for sent_idx, sentence in enumerate(lowerCamelCase					):
													chars.extend(self.process_string(lowerCamelCase					)					)
													if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase					) - 1:
																chars.append(self.sentence_delimiter					)
										return chars





		_lowercase			:			Optional[int]										=						tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		_lowercase			:			List[str]										=						tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


_lowercase			:			Dict										=						"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

_lowercase			:			List[Any]										=						"\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

_lowercase			:			Dict										=						"\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"

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


					'''simple docstring'''





					def 		snake_case	(	self							:  Dict					)-> Optional[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'''					),
								        }					),  codebase_urls=['''https://github.com/jitsi/jiwer/'''],  reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								        '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
								    ],  )




					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict=False					)-> List[Any]:
								if concatenate_texts:
											return jiwer.compute_measures(
											    lowerCamelCase,  lowerCamelCase,  truth_transform=lowerCamelCase,  hypothesis_transform=lowerCamelCase,  )["wer"]

								lowerCamelCase__		:		Optional[Any]		     =0
								lowerCamelCase__		:		Union[str, Any]		     =0
								for prediction, reference in zip(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		int		     =jiwer.compute_measures(
											    lowerCamelCase,  lowerCamelCase,  truth_transform=lowerCamelCase,  hypothesis_transform=lowerCamelCase,  )
											incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
											total += measures["substitutions"] + measures["deletions"] + measures["hits"]

								return incorrect / total




