


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
)
UpperCAmelCase__     			=							logging.getLogger(__name__)
def        UpperCAmelCase_      (							)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''		)
 parser.add_argument('''--model_name'''  ,		type=UpperCamelCase_  ,		default='''transfo-xl-wt103'''  ,		help='''pretrained model name'''		)
 parser.add_argument(
     '''--split'''  ,		type=UpperCamelCase_  ,		default='''test'''  ,		choices=['''all''', '''valid''', '''test''']  ,		help='''which split to evaluate'''		)
 parser.add_argument('''--batch_size'''  ,		type=UpperCamelCase_  ,		default=10  ,		help='''batch size'''		)
 parser.add_argument('''--tgt_len'''  ,		type=UpperCamelCase_  ,		default=128  ,		help='''number of tokens to predict'''		)
 parser.add_argument('''--ext_len'''  ,		type=UpperCamelCase_  ,		default=0  ,		help='''length of the extended context'''		)
 parser.add_argument('''--mem_len'''  ,		type=UpperCamelCase_  ,		default=1600  ,		help='''length of the retained previous heads'''		)
 parser.add_argument('''--clamp_len'''  ,		type=UpperCamelCase_  ,		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=UpperCamelCase_  ,		required=UpperCamelCase_  ,		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=UpperCamelCase_  ,		default=''''''  ,		help='''Can be used for distant debugging.'''		)
 parser.add_argument('''--server_port'''  ,		type=UpperCamelCase_  ,		default=''''''  ,		help='''Can be used for distant debugging.'''		)
 _lowercase							   =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=UpperCamelCase_		)
  ptvsd.wait_for_attach()

 _lowercase							   =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''		)
 logger.info('''device: {}'''.format(UpperCamelCase_		)		)

 # 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 )
 _lowercase							   =TransfoXLCorpus.from_pretrained(args.model_name		)

 _lowercase							   =corpus.get_iterator('''valid'''  ,		args.batch_size  ,		args.tgt_len  ,		device=UpperCamelCase_  ,		ext_len=args.ext_len		)
 _lowercase							   =corpus.get_iterator('''test'''  ,		args.batch_size  ,		args.tgt_len  ,		device=UpperCamelCase_  ,		ext_len=args.ext_len		)

 # Load a pre-trained model
 _lowercase							   =TransfoXLLMHeadModel.from_pretrained(args.model_name		)
 model.to(UpperCamelCase_		)

 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:
  _lowercase							   =args.clamp_len
 if args.same_length:
  _lowercase							   =True

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

 # Run on test data.
 if args.split == "all":
  _lowercase							   =evaluate(UpperCamelCase_		)
  _lowercase							   =evaluate(UpperCamelCase_		)
 elif args.split == "valid":
  _lowercase							   =evaluate(UpperCamelCase_		)
  _lowercase							   =None
 elif args.split == "test":
  _lowercase							   =evaluate(UpperCamelCase_		)
  _lowercase							   =None

 def format_log(__snake_case  ,		__snake_case		):
  _lowercase							   ='''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(UpperCamelCase_  ,		UpperCamelCase_  ,		math.exp(UpperCamelCase_		)		)
  return log_str

 _lowercase							   =''''''
 if valid_loss is not None:
  log_str += format_log(UpperCamelCase_  ,		'''valid'''		)
 if test_loss is not None:
  log_str += format_log(UpperCamelCase_  ,		'''test'''		)

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


if __name__ == "__main__":
  main()










UpperCAmelCase__     			=							[0.33, 0.44, 0.55, 0.44, 0.33]
UpperCAmelCase__     			=							3
UpperCAmelCase__     			=							5

# Buffer Space (with Parameters Space)
UpperCAmelCase__     			=							[]
UpperCAmelCase__     			=							[]

# Machine Time
UpperCAmelCase__     			=							0
def        UpperCAmelCase_      (							__snake_case		)  ->    Tuple:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 # Choosing Dynamical Systems (All)
 for key, value in enumerate(__snake_case		):
  # Evolution Parameter
  _lowercase							   =float(seed / value		)

  # Control Theory: Orbit Change
  _lowercase							   =(buffer_space[(key + 1) % m] + e) % 1

  # Control Theory: Trajectory Change
  _lowercase							   =(params_space[key] + e) % 1 + 3

  # Modification (Transition Function) - Jumps
  _lowercase							   =round(float(r * value * (1 - value)		)  ,		10		)
  _lowercase							   =r  # Saving to Parameters Space

 # Logistic Map
 assert max(__snake_case		) < 1
 assert max(__snake_case		) < 4

 # Machine Time
 machine_time += 1
def        UpperCAmelCase_      (							)  ->    Union[str, Any]:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 # PRNG (Xorshift by George Marsaglia)
 def xorshift(__snake_case  ,		__snake_case		):
  x ^= y >> 13
  y ^= x << 17
  x ^= y >> 5
  return x

 # Choosing Dynamical Systems (Increment)
 _lowercase							   =machine_time % m

 # Evolution (Time Length)
 for _ in range(0  ,		__snake_case		):
  # Variables (Position + Parameters)
  _lowercase							   =params_space[key]
  _lowercase							   =buffer_space[key]

  # Modification (Transition Function) - Flow
  _lowercase							   =round(float(r * value * (1 - value)		)  ,		10		)
  _lowercase							   =(machine_time * 0.01 + r * 1.01) % 1 + 3

 # Choosing Chaotic Data
 _lowercase							   =int(buffer_space[(key + 2) % m] * (10**10)		)
 _lowercase							   =int(buffer_space[(key - 2) % m] * (10**10)		)

 # Machine Time
 machine_time += 1

 return xorshift(__snake_case  ,		__snake_case		) % 0XF_F_F_F_F_F_F_F
def        UpperCAmelCase_      (							)  ->    int:







 """simple docstring"""


 global buffer_space, params_space, machine_time, K, m, t

 _lowercase							   =K
 _lowercase							   =[0] * m
 _lowercase							   =0


if __name__ == "__main__":
  # Initialization
  reset()

  # Pushing Data (Input)
  import random

  UpperCAmelCase__     			=							random.sample(range(0xffff_ffff), 100)
  for chunk in message:
    push(chunk)

  # for controlling
  UpperCAmelCase__     			=							''''''

  # Pulling Data (Output)
  while inp in ("e", "E"):
    print(f'''{format(pull(), '#04x')}''')
    print(buffer_space)
    print(params_space)
    UpperCAmelCase__     			=							input('''(e)exit? ''').strip()

