



'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[str]=1_0_2_4  ,						UpperCamelCase__      :					int=1_0_2_4  ,						UpperCamelCase__      :					Union[str, Any]=3.6 ):


       """simple docstring"""







       UpperCamelCase					=  tokenizer
       UpperCamelCase					=  tokenizer.bos_token_id
       UpperCamelCase					=  dataset
       UpperCamelCase					=  seq_length
       UpperCamelCase					=  seq_length * chars_per_token * num_of_sequences






    def __iter__(  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  iter(self.dataset )
       UpperCamelCase					=  True
       while more_examples:
          UpperCamelCase				,      UpperCamelCase					=  [], 0
          while True:
             if buffer_len >= self.input_characters:
                break
             try:
                buffer.append(next(__a )['content'] )
                buffer_len += len(buffer[-1] )
             except StopIteration:
                UpperCamelCase					=  False
                break
          UpperCamelCase					=  tokenizer(__a  ,						truncation=__a )['input_ids']
          UpperCamelCase					=  []
          for tokenized_input in tokenized_inputs:
             all_token_ids.extend(tokenized_input + [self.concat_token_id] )
          for i in range(0  ,						len(__a )  ,						self.seq_length ):
             UpperCamelCase					=  all_token_ids[i : i + self.seq_length]
             if len(__a ) == self.seq_length:
                yield torch.tensor(__a )







def 				__lowerCamelCase				(   A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  {'streaming': True}
   UpperCamelCase					=  load_dataset(args.dataset_name    ,						split='train'    ,						**__snake_case    )
   UpperCamelCase					=  ConstantLengthDataset(__snake_case    ,						__snake_case    ,						seq_length=args.seq_length    )
   UpperCamelCase					=  DataLoader(__snake_case    ,						batch_size=args.batch_size    )
   return eval_dataloader







def 				__lowerCamelCase				(   A__    )       ->	Optional[Any]:






   """simple docstring"""
   model.eval()
   UpperCamelCase					=  []
   for step, batch in enumerate(__snake_case    ):
      with torch.no_grad():
         UpperCamelCase					=  model(__snake_case    ,						labels=__snake_case    )
      UpperCamelCase					=  outputs.loss.repeat(args.batch_size    )
      losses.append(accelerator.gather(__snake_case    )    )

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   UpperCamelCase					=  torch.mean(torch.cat(__snake_case    )    )
   try:
      UpperCamelCase					=  torch.exp(__snake_case    )
   except OverflowError:
      UpperCamelCase					=  float('inf'    )
   return loss.item(), perplexity.item()


# Setup Accelerator
_lowerCamelCase	:       List[str]      	=	Accelerator()

# Parse configuration
_lowerCamelCase	:       str      	=	HfArgumentParser(EvaluationArguments)
_lowerCamelCase	:       Union[str, Any]      	=	parser.parse_args()
set_seed(args.seed)

# Logging
_lowerCamelCase	:       Any      	=	logging.getLogger(__name__)
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)

# Load model and tokenizer
_lowerCamelCase	:       Optional[Any]      	=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowerCamelCase	:       Union[str, Any]      	=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_lowerCamelCase	:       Union[str, Any]      	=	create_dataloader(args)

# Prepare everything with our `accelerator`.
_lowerCamelCase	,_lowerCamelCase	:       str      	=	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_lowerCamelCase	,_lowerCamelCase	:       Optional[Any]      	=	evaluate(args)
logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')



"""simple docstring"""





from collections import defaultdict






class     _UpperCAmelCase:



		def __init__(					self				,		__a				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  total  # total no of tasks (N)

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

							_UpperCamelCase							 =  defaultdict(__a)  # 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
							_UpperCamelCase							 =  (1 << len(__a)) - 1



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							# 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
							_UpperCamelCase							 =  self.count_ways_until(__a				,		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.
							_UpperCamelCase							 =  total_ways_util

							return self.dp[mask][task_no]




		def 						UpperCAmelCase							(					self				,		__a)       ->       int:






							'''simple docstring'''
							# Store the list of persons for each task
							for i in range(len(__a)):
												for j in task_performed[i]:
																	self.task[j].append(__a)

        # 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														=		5  # total no of tasks (the value of N)

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






