






"""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		(				_a	):


	'''simple docstring'''





	def __init__(     self      ,      _A      ,      _A      ,      _A=1_0_2_4      ,      _A=1_0_2_4      ,      _A=3.6					):



		'''simple docstring'''

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







	def __iter__(     self					):



		'''simple docstring'''

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






def 					_lowerCAmelCase(a   :    Tuple  )      ->       Optional[Any]:
	_SCREAMING_SNAKE_CASE						={'''streaming''': True}
	_SCREAMING_SNAKE_CASE						=load_dataset(args.dataset_name			,      split='''train'''			,      **a__  )
	_SCREAMING_SNAKE_CASE						=ConstantLengthDataset(a__			,      a__			,      seq_length=args.seq_length  )
	_SCREAMING_SNAKE_CASE						=DataLoader(a__			,      batch_size=args.batch_size  )
	return eval_dataloader






def 					_lowerCAmelCase(a   :    Any  )      ->       Dict:
	model.eval()
	_SCREAMING_SNAKE_CASE						=[]
	for step, batch in enumerate(a__  ):
		with torch.no_grad():
			_SCREAMING_SNAKE_CASE						=model(a__			,      labels=a__  )
		_SCREAMING_SNAKE_CASE						=outputs.loss.repeat(args.batch_size  )
		losses.append(accelerator.gather(a__  )  )

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


# Setup Accelerator
UpperCAmelCase_       :							Any             =				Accelerator()

# Parse configuration
UpperCAmelCase_       :							Optional[Any]             =				HfArgumentParser(EvaluationArguments)
UpperCAmelCase_       :							int             =				parser.parse_args()
set_seed(args.seed)

# Logging
UpperCAmelCase_       :							List[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
UpperCAmelCase_       :							Union[str, Any]             =				AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_       :							Dict             =				AutoTokenizer.from_pretrained(args.model_ckpt)

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

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

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
UpperCAmelCase_      ,       UpperCAmelCase_       :							Any             =				evaluate(args)






logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")











from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__           =			10





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							for i in range(a__ ,      a__							):
														if array[i] == target:
																					return i
							return -1





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      len(a__							)
							while left <= right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)

														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					UpperCamelCase__					  =      one_third - 1
														elif array[two_third] < target:
																					UpperCamelCase__					  =      two_third + 1

														else:
																					UpperCamelCase__					  =      one_third + 1
																					UpperCamelCase__					  =      two_third - 1
							else:
														return -1





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							if left < right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)
														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(a__ ,      one_third - 1 ,      a__ ,      a__							)
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1 ,      a__ ,      a__ ,      a__							)
														else:
																					return rec_ternary_search(one_third + 1 ,      two_third - 1 ,      a__ ,      a__							)
							else:
														return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase__           =			input("Enter numbers separated by comma:\n").strip()
					UpperCamelCase__           =			[int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					UpperCamelCase__           =			int(input("Enter the number to be found in the list:\n").strip())
					UpperCamelCase__           =			ite_ternary_search(collection, target)
					UpperCamelCase__           =			rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f"""Iterative search: {target} found at positions: {resulta}""")
										print(f"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print("Not found")




