


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
)
a_       :List[str]				  =				logging.getLogger(__name__)
def 	lowercase_							():
       snake_case__							:     int            =     argparse.ArgumentParser(description='PyTorch Transformer Language Model'				)
       parser.add_argument('--model_name'		, type=A		, default='transfo-xl-wt103'		, help='pretrained model name'				)
       parser.add_argument(
           '--split'		, type=A		, default='test'		, choices=['all', 'valid', 'test']		, help='which split to evaluate'				)
       parser.add_argument('--batch_size'		, type=A		, default=1_0		, help='batch size'				)
       parser.add_argument('--tgt_len'		, type=A		, default=1_2_8		, help='number of tokens to predict'				)
       parser.add_argument('--ext_len'		, type=A		, default=0		, help='length of the extended context'				)
       parser.add_argument('--mem_len'		, type=A		, default=1_6_0_0		, help='length of the retained previous heads'				)
       parser.add_argument('--clamp_len'		, type=A		, default=1_0_0_0		, 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=A		, required=A		, 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=A		, default=''		, help='Can be used for distant debugging.'				)
       parser.add_argument('--server_port'		, type=A		, default=''		, help='Can be used for distant debugging.'				)
       snake_case__							:     Dict            =     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=A				)
              ptvsd.wait_for_attach()

       snake_case__							:     Optional[int]            =     torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
       logger.info('device: {}'.format(A				)				)

       # 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 )
       snake_case__							:     Dict            =     TransfoXLCorpus.from_pretrained(args.model_name				)

       snake_case__							:     Dict            =     corpus.get_iterator('valid'		, args.batch_size		, args.tgt_len		, device=A		, ext_len=args.ext_len				)
       snake_case__							:     List[Any]            =     corpus.get_iterator('test'		, args.batch_size		, args.tgt_len		, device=A		, ext_len=args.ext_len				)

       # Load a pre-trained model
       snake_case__							:     str            =     TransfoXLLMHeadModel.from_pretrained(args.model_name				)
       model.to(A				)

       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:
              snake_case__							:     int            =     args.clamp_len
       if args.same_length:
              snake_case__							:     Union[str, Any]            =     True

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(A			:			List[Any]				):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              snake_case__							:     Tuple            =     0, 0.0
              snake_case__							:     List[Any]            =     time.time()
              with torch.no_grad():
                     snake_case__							:     Optional[Any]            =     None
                     for idx, (data, target, seq_len) in enumerate(A				):
                            snake_case__							:     str            =     model(A		, lm_labels=A		, mems=A				)
                            snake_case__							:     str            =     ret
                            snake_case__							:     List[Any]            =     loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     snake_case__							:     Dict            =     time.time() - start_time
              logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A		, 1_0_0_0 * total_time / (idx + 1)				)				)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              snake_case__							:     int            =     evaluate(A				)
              snake_case__							:     int            =     evaluate(A				)
       elif args.split == "valid":
              snake_case__							:     Union[str, Any]            =     evaluate(A				)
              snake_case__							:     Optional[Any]            =     None
       elif args.split == "test":
              snake_case__							:     Tuple            =     evaluate(A				)
              snake_case__							:     List[str]            =     None

       def format_log(A			:			Dict		, A			:			List[str]				):
              snake_case__							:     Optional[Any]            =     '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(A		, A		, math.exp(A				)				)
              return log_str

       snake_case__							:     str            =     ''
       if valid_loss is not None:
              log_str += format_log(A		, 'valid'				)
       if test_loss is not None:
              log_str += format_log(A		, 'test'				)

       logger.info('=' * 1_0_0				)
       logger.info(A				)
       logger.info('=' * 1_0_0				)


if __name__ == "__main__":
 main()











import math
def 	lowercase_							(A			:			int				):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






