"""simple docstring"""



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase=None			,  lowerCAmelCase=None							):
							return field(default_factory=lambda: default			,  metadata=lowerCAmelCase							)



@dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				list_field(
			    default=[]							,   metadata={
			        'help': (
			            'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
			            ' of all available models'
			        )
			    }							,   )

			_UpperCamelCase  :						List[int]					     =				list_field(
			    default=[8]							,   metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}     )

			_UpperCamelCase  :						List[int]					     =				list_field(
			    default=[8, 32, 128, 512]							,   metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}							,   )

			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}							,   )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}							,   )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Use FP16 to accelerate inference.'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Benchmark training of model'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Verbose memory tracing'}     )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}							,   )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={
			        'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
			    }							,   )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Trace memory line by line'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Save result to a CSV file'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Save all print statements in a log file'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   metadata={'help': 'Whether to print environment information'}     )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={
			        'help': (
			            'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
			            ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
			            ' for debugging / testing and on TPU.'
			        )
			    }							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"inference_time_{round(time()     )}.csv"							,   metadata={'help': 'CSV filename used if saving time results to csv.'}							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"inference_memory_{round(time()     )}.csv"							,   metadata={'help': 'CSV filename used if saving memory results to csv.'}							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"train_time_{round(time()     )}.csv"							,   metadata={'help': 'CSV filename used if saving time results to csv for training.'}							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"train_memory_{round(time()     )}.csv"							,   metadata={'help': 'CSV filename used if saving memory results to csv for training.'}							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"env_info_{round(time()     )}.csv"							,   metadata={'help': 'CSV filename used if saving environment information.'}							,   )
			_UpperCamelCase  :						str					     =				field(
			    default=F"log_{round(time()     )}.csv"							,   metadata={'help': 'Log filename used if print statements are saved in log.'}							,   )
			_UpperCamelCase  :						int					     =				field(default=3							,   metadata={'help': 'Times an experiment will be run.'}     )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={
			        'help': (
			            'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
			            ' model weights.'
			        )
			    }							,   )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										warnings.warn(
										    F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
										    """ are deprecated in general and it is advised to use external Benchmarking libraries """
										    """ to benchmark Transformer models."""   ,      snake_case_   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return json.dumps(dataclasses.asdict(self							)   ,      indent=2							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if len(self.models							) <= 0:
																	raise ValueError(
																	    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
																	    """ bert-base-cased` or `args.models = ['bert-base-cased']."""							)
										return self.models





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if not self.multi_process:
																	return False
										elif self.is_tpu:
																	logger.info("""Multiprocessing is currently not possible on TPU."""							)
																	return False
										else:
																	return True






"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

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

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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''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
										A__       :     Optional[int]								=			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.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''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__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

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





