



'''simple docstring'''







import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      argparse.ArgumentParser(description='PyTorch Transformer Language Model'     )
		parser.add_argument('--model_name' ,		type=_snake_case ,		default='transfo-xl-wt103' ,		help='pretrained model name'     )
		parser.add_argument(
		    '--split' ,		type=_snake_case ,		default='test' ,		choices=['all', 'valid', 'test'] ,		help='which split to evaluate'     )
		parser.add_argument('--batch_size' ,		type=_snake_case ,		default=10 ,		help='batch size'     )
		parser.add_argument('--tgt_len' ,		type=_snake_case ,		default=128 ,		help='number of tokens to predict'     )
		parser.add_argument('--ext_len' ,		type=_snake_case ,		default=0 ,		help='length of the extended context'     )
		parser.add_argument('--mem_len' ,		type=_snake_case ,		default=1600 ,		help='length of the retained previous heads'     )
		parser.add_argument('--clamp_len' ,		type=_snake_case ,		default=1000 ,		help='max positional embedding index'     )
		parser.add_argument('--no_cuda' ,		action='store_true' ,		help='Do not use CUDA even though CUA is available'     )
		parser.add_argument('--work_dir' ,		type=_snake_case ,		required=_snake_case ,		help='path to the work_dir'     )
		parser.add_argument('--no_log' ,		action='store_true' ,		help='do not log the eval result'     )
		parser.add_argument('--same_length' ,		action='store_true' ,		help='set same length attention with masking'     )
		parser.add_argument('--server_ip' ,		type=_snake_case ,		default='' ,		help='Can be used for distant debugging.'     )
		parser.add_argument('--server_port' ,		type=_snake_case ,		default='' ,		help='Can be used for distant debugging.'     )
		SCREAMING_SNAKE_CASE_						:str										=      parser.parse_args()
		assert args.ext_len >= 0, "extended context length must be non-negative"

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('Waiting for debugger attach'     )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,		redirect_output=_snake_case     )
				ptvsd.wait_for_attach()

		SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )
		logger.info('device: {}'.format(_snake_case     )     )

		# Load a pre-processed dataset
		# You can also build the corpus yourself using TransfoXLCorpus methods
		# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
		# and tokenizing the dataset
		# The pre-processed corpus is a convertion (using the conversion script )
		SCREAMING_SNAKE_CASE_						:int										=      TransfoXLCorpus.from_pretrained(args.model_name     )

		SCREAMING_SNAKE_CASE_						:int										=      corpus.get_iterator('valid' ,		args.batch_size ,		args.tgt_len ,		device=_snake_case ,		ext_len=args.ext_len     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      corpus.get_iterator('test' ,		args.batch_size ,		args.tgt_len ,		device=_snake_case ,		ext_len=args.ext_len     )

		# Load a pre-trained model
		SCREAMING_SNAKE_CASE_						:Dict										=      TransfoXLLMHeadModel.from_pretrained(args.model_name     )
		model.to(_snake_case     )

		logger.info(
		    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
		        args.batch_size ,		args.tgt_len ,		args.ext_len ,		args.mem_len ,		args.clamp_len     )     )

		model.reset_memory_length(args.mem_len     )
		if args.clamp_len > 0:
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      args.clamp_len
		if args.same_length:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      True

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(SCREAMING_SNAKE_CASE     ):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				SCREAMING_SNAKE_CASE_						:Tuple										=      0, 0.0
				SCREAMING_SNAKE_CASE_						:Tuple										=      time.time()
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_						:str										=      None
						for idx, (data, target, seq_len) in enumerate(_snake_case     ):
								SCREAMING_SNAKE_CASE_						:Any										=      model(_snake_case ,		lm_labels=_snake_case ,		mems=_snake_case     )
								SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ret
								SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						SCREAMING_SNAKE_CASE_						:Optional[Any]										=      time.time() - start_time
				logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_snake_case ,		1000 * total_time / (idx + 1)     )     )
				return total_loss / total_len

		# Run on test data.
		if args.split == "all":
				SCREAMING_SNAKE_CASE_						:str										=      evaluate(_snake_case     )
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      evaluate(_snake_case     )
		elif args.split == "valid":
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      evaluate(_snake_case     )
				SCREAMING_SNAKE_CASE_						:List[str]										=      None
		elif args.split == "test":
				SCREAMING_SNAKE_CASE_						:List[str]										=      evaluate(_snake_case     )
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      None

		def format_log(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Any										=      '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_snake_case ,		_snake_case ,		math.exp(_snake_case     )     )
				return log_str

		SCREAMING_SNAKE_CASE_						:int										=      ''''''
		if valid_loss is not None:
				log_str += format_log(_snake_case ,		'valid'     )
		if test_loss is not None:
				log_str += format_log(_snake_case ,		'test'     )

		logger.info('=' * 100     )
		logger.info(_snake_case     )
		logger.info('=' * 100     )


if __name__ == "__main__":
							main()




"""simple docstring"""





from collections.abc import Callable

snake_case__		:  Optional[int]     					=					Callable[[float], float]  # type alias for a real -> real function


def   _snake_case    (    _snake_case					:      RealFunc			, _snake_case					:      RealFunc			, _snake_case					:      int			, ):
      lowerCAmelCase       :						Dict							= float(_snake_case      )
      while True:
            try:
                  lowerCAmelCase       :						Optional[Any]							= prev_guess - function(_snake_case      ) / derivative(_snake_case      )
            except ZeroDivisionError:
                  raise ZeroDivisionError('''Could not find root'''      ) from None
            if abs(prev_guess - next_guess      ) < 10**-5:
                  return next_guess
            lowerCAmelCase       :						Optional[int]							= next_guess


def   _snake_case    (    _snake_case					:      float      ):
      return (x**3) - (2 * x) - 5


def   _snake_case    (    _snake_case					:      float      ):
      return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


