

'''simple docstring'''


from collections import defaultdict




class      a      :






						"""simple docstring"""




						def __init__(				self       :   int      ,      snake_case_       :   Dict      ,      snake_case_       :   Optional[Any]	):



								'''simple docstring'''



								snake_case__    :							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
								snake_case__    :							str							    =	[
								    [-1 for i in range(total + 1	)] for j in range(2 ** len(snake_case_	)	)
								]

								snake_case__    :							Optional[int]							    =	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
								snake_case__    :							Dict							    =	(1 << len(snake_case_	)) - 1







						def    __magic_name__				(				self       :   Union[str, Any]      ,      snake_case_       :   Union[str, Any]      ,      snake_case_       :   str	):



								'''simple docstring'''



								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__    :							Tuple							    =	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.
								snake_case__    :							Any							    =	total_ways_util

								return self.dp[mask][task_no]







						def    __magic_name__				(				self       :   int      ,      snake_case_       :   List[str]	):



								'''simple docstring'''



								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__":
					lowerCAmelCase__    :							Dict					    =    5  # total no of tasks (the value of N)

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










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      act_info[0] == "gated"

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
           raise ValueError(
               f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
           A_       :					Any        =      "past_encoder_sequence + sequence"
           A_       :					Union[str, Any]        =      {0: "batch"}
           A_       :					Optional[int]        =      {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           A_       :					Dict        =      {0: "batch", 1: "decoder_sequence"}
           A_       :					str        =      {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
