

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_(			)       -> Any:
 UpperCAmelCase__							=				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=1_28  ,    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=16_00  ,    help='length of the retained previous heads'   )
 parser.add_argument('--clamp_len'  ,    type=snake_case__  ,    default=10_00  ,    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.'   )
 UpperCAmelCase__							=				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()

 UpperCAmelCase__							=				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 )
 UpperCAmelCase__							=				TransfoXLCorpus.from_pretrained(args.model_name   )

 UpperCAmelCase__							=				corpus.get_iterator('valid'  ,    args.batch_size  ,    args.tgt_len  ,    device=snake_case__  ,    ext_len=args.ext_len   )
 UpperCAmelCase__							=				corpus.get_iterator('test'  ,    args.batch_size  ,    args.tgt_len  ,    device=snake_case__  ,    ext_len=args.ext_len   )

 # Load a pre-trained model
 UpperCAmelCase__							=				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:
  UpperCAmelCase__							=				args.clamp_len
 if args.same_length:
  UpperCAmelCase__							=				True

 ###############################################################################
 # Evaluation code
 ###############################################################################
 def evaluate(snake_case__:  Optional[Any]   ):
  # Turn on evaluation mode which disables dropout.
  model.eval()
  UpperCAmelCase__				,    UpperCAmelCase__							=				0, 0.0
  UpperCAmelCase__							=				time.time()
  with torch.no_grad():
   UpperCAmelCase__							=				None
   for idx, (data, target, seq_len) in enumerate(snake_case__   ):
    UpperCAmelCase__							=				model(snake_case__  ,    lm_labels=snake_case__  ,    mems=snake_case__   )
    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__							=				ret
    UpperCAmelCase__							=				loss.mean()
    total_loss += seq_len * loss.item()
    total_len += seq_len
   UpperCAmelCase__							=				time.time() - start_time
  logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(snake_case__  ,    10_00 * total_time / (idx + 1)   )   )
  return total_loss / total_len

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

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

 UpperCAmelCase__							=				''
 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('=' * 1_00   )
 logger.info(snake_case__   )
 logger.info('=' * 1_00   )


if __name__ == "__main__":
 main()









from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
_lowerCamelCase			 =						TypeVar('T', bound='Enum')



@unique
class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[Any]   		=  80
     lowerCAmelCase				:       List[str]   		=  70
     lowerCAmelCase				:       Optional[Any]   		=  60
     lowerCAmelCase				:       Optional[Any]   		=  50
     lowerCAmelCase				:       List[Any]   		=  40
     lowerCAmelCase				:       Dict   		=  30
     lowerCAmelCase				:       List[Any]   		=  20
     lowerCAmelCase				:       List[Any]   		=  10



@unique
class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Dict   		=  24
     lowerCAmelCase				:       Union[str, Any]   		=  21
     lowerCAmelCase				:       Tuple   		=  18
     lowerCAmelCase				:       str   		=  15
     lowerCAmelCase				:       Dict   		=  12
     lowerCAmelCase				:       Dict   		=  9
     lowerCAmelCase				:       Tuple   		=  6
     lowerCAmelCase				:       Any   		=  3
     lowerCAmelCase				:       List[str]   		=  2
     lowerCAmelCase				:       Any   		=  1
     lowerCAmelCase				:       str   		=  -1
     lowerCAmelCase				:       Any   		=  -2
     lowerCAmelCase				:       List[str]   		=  -3
     lowerCAmelCase				:       Dict   		=  -6
     lowerCAmelCase				:       int   		=  -9
     lowerCAmelCase				:       Optional[Any]   		=  -12
     lowerCAmelCase				:       List[str]   		=  -15
     lowerCAmelCase				:       Any   		=  -18
     lowerCAmelCase				:       int   		=  -21
     lowerCAmelCase				:       Union[str, Any]   		=  -24
     @classmethod
     def   lowerCamelCase_   (	cls   :			type[T]				):
         return {unit.name: unit.value for unit in cls if unit.value > 0}




     @classmethod
     def   lowerCamelCase_   (	cls   :			type[T]				):
         return {unit.name: unit.value for unit in cls if unit.value < 0}




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float      )  ->							str:
    UpperCAmelCase_							 =     SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        UpperCAmelCase_							 =     value / (10**value_prefix)
        if numerical_part > 1:
            return f'{numerical_part!s} {name_prefix}'
    return str(__UpperCamelCase      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float      )  ->							str:
    for prefix in BinaryUnit:
        UpperCAmelCase_							 =     value / (2**prefix.value)
        if numerical_part > 1:
            return f'{numerical_part!s} {prefix.name}'
    return str(__UpperCamelCase      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





