




from collections import defaultdict







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      total  # total no of tasks (N)

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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      defaultdict(snake_case__ )  # 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
							SCREAMING_SNAKE_CASE_   : str					  =      (1 << len(snake_case__ )) - 1






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							# 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
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.count_ways_until(snake_case__      ,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.
							SCREAMING_SNAKE_CASE_   : Dict					  =      total_ways_util

							return self.dp[mask][task_no]






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

        # 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__ :   Optional[Any]						=							5  # total no of tasks (the value of N)

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







import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE							    =							datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

SCREAMING_SNAKE_CASE							    =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

SCREAMING_SNAKE_CASE							    =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,       citation=_CITATION  ,       homepage="""https://unbabel.github.io/COMET/html/index.html"""  ,       inputs_description=_KWARGS_DESCRIPTION  ,       features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """predictions""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """references""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                }    )  ,       codebase_urls=["""https://github.com/Unbabel/COMET"""]  ,       reference_urls=[
                """https://github.com/Unbabel/COMET""",
                """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
                """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
            ]  ,       )


     def 	snake_case_  (	self  ,       __A    ):
        if self.config_name == "default":
           __a					=  comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""    )    )
        else:
           __a					=  comet.load_from_checkpoint(comet.download_model(self.config_name    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       __A=False    ):
        if gpus is None:
           __a					=  1 if torch.cuda.is_available() else 0
        __a					=  {"""src""": sources, """mt""": predictions, """ref""": references}
        __a					=  [dict(zip(__A  ,       __A    )    ) for t in zip(*data.values()    )]
        __a	,    __a					=  self.scorer.predict(__A  ,       gpus=__A  ,       progress_bar=__A    )
        return {"mean_score": mean_score, "scores": scores}



