





"""simple docstring"""




from collections import defaultdict


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								a      :		Optional[int]	=				total  # total no of tasks (N)

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

								a      :		int	=				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
								a      :		List[str]	=				(1 << len(lowerCAmelCase__      )) - 1






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Dict:
								# 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
								a      :		str	=				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.
								a      :		List[str]	=				total_ways_util

								return self.dp[mask][task_no]






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[Any]:
								# 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__":
		a						: List[Any]     	=			5  # total no of tasks (the value of N)

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








'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							a  :				Dict									=  None


a  :				List[Any]									=  logging.get_logger(__name__)

a  :				List[Any]									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a  :				str									=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a  :				List[Any]									=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]
				snake_case_      =		TaTokenizer

				snake_case_      =		[]


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					List[Any]="<unk>"       ,				lowercase_    :					int="<pad>"       ,				lowercase_    :					int=100       ,				lowercase_    :					List[Any]=None       ,				**lowercase_    :					List[str]       ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
														snake_case_									=				[F"<extra_id_{i}>" for i in range(lowercase_					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														snake_case_									=				len(set(filter(lambda lowercase_					: bool('''extra_id_''' in str(lowercase_					)					)       ,				lowercase_					)					)					)
														if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																			    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																			    ''' tokens'''					)

									super().__init__(
									    lowercase_       ,				tokenizer_file=lowercase_       ,				eos_token=lowercase_       ,				unk_token=lowercase_       ,				pad_token=lowercase_       ,				extra_ids=lowercase_       ,				additional_special_tokens=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				False if not self.vocab_file else True
									snake_case_									=				extra_ids


				@staticmethod
				def   A_ (		lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														snake_case_									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
														elif init_max_model_length is None:
																			warnings.warn(
																			    '''This tokenizer was incorrectly instantiated with a model max length of'''
																			    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																			    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																			    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																			    F" {pretrained_model_name_or_path} automatically truncating your input to"
																			    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																			    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																			    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																			    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,				lowercase_       ,				)

									return max_model_length


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''					)

									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
														copyfile(self.vocab_file       ,				lowercase_					)
														logger.info(F"Copy vocab file to {out_vocab_file}"					)

									return (out_vocab_file,)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
									else:
														snake_case_									=				token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a


				def   A_ (		self    :					int       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.eos_token_id]

									if token_ids_a is None:
														return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


				def   A_ (		self    :					Dict					):
									return list(
									    set(filter(lambda lowercase_					: bool(re.search(R'''<extra_id_\d+>'''       ,				lowercase_					)					) is not None       ,				self.additional_special_tokens					)					)					)


				def   A_ (		self    :					Any					):
									return [self.convert_tokens_to_ids(lowercase_					) for token in self.get_sentinel_tokens()]

