
"""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     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a=10_24				,		__a=10_24				,		__a=3.6)       ->       Any:






							'''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)       ->       List[str]:






							'''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__		(			__snake_case	)							->		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__		(			__snake_case	)							->		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
_a														=		Accelerator()

# Parse configuration
_a														=		HfArgumentParser(EvaluationArguments)
_a														=		parser.parse_args()
set_seed(args.seed)

# Logging
_a														=		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
_a														=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_a														=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_a														=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_a							,							_a														=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_a							,							_a														=		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
				    )
				)






