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

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

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

   __lowerCamelCase    		=					corpus.get_iterator('valid'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)
   __lowerCamelCase    		=					corpus.get_iterator('test'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)

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

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

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

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

   def format_log(lowercase_     :	Tuple      , lowercase_     :	Dict						):
      __lowerCamelCase    		=					'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase_      , lowercase_      , math.exp(lowercase_						)						)
      return log_str

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

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


if __name__ == "__main__":
     main()













import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       0  # Number of params in the encoder

     print('''name'''.ljust(60 ,     ''' '''						) ,     '''Remaining Weights %''' ,     '''Remaining Weight'''						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (mask > 0.0).sum().item()
               else:
                    raise ValueError('''Unknown pruning method'''						)
               remaining_count += mask_ones
               print(name.ljust(60 ,     ''' '''						) ,     str(round(100 * mask_ones / param.numel() ,     3						)						).ljust(20 ,     ''' '''						) ,     str(snake_case_						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''''''						)
     print('''Remaining Weights (global) %: ''' ,     100 * remaining_count / encoder_count						)


if __name__ == "__main__":
    a_    :				Optional[Any]					=			argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
            'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
            'Not needed for `l0`'
        ),
    )
    parser.add_argument(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)