


'''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
)
__lowercase:		int    						=  logging.getLogger(__name__)



def 							SCREAMING_SNAKE_CASE__(		)		->       Optional[int]:





   '''simple docstring'''







   UpperCamelCase__			    =    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=1_28     ,		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=16_00     ,		help="length of the retained previous heads" )
   parser.add_argument("--clamp_len"     ,		type=_UpperCamelCase     ,		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=_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." )
   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=_UpperCamelCase )
      ptvsd.wait_for_attach()

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

   UpperCamelCase__			    =    corpus.get_iterator("valid"     ,		args.batch_size     ,		args.tgt_len     ,		device=_UpperCamelCase     ,		ext_len=args.ext_len )
   UpperCamelCase__			    =    corpus.get_iterator("test"     ,		args.batch_size     ,		args.tgt_len     ,		device=_UpperCamelCase     ,		ext_len=args.ext_len )

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

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(_UpperCamelCase   :					Tuple ):
      # 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(_UpperCamelCase ):
            UpperCamelCase__			    =    model(_UpperCamelCase     ,		lm_labels=_UpperCamelCase     ,		mems=_UpperCamelCase )
            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(_UpperCamelCase     ,		10_00 * total_time / (idx + 1) ) )
      return total_loss / total_len

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

   def format_log(_UpperCamelCase   :					str     ,		_UpperCamelCase   :					Tuple ):
      UpperCamelCase__			    =    "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(_UpperCamelCase     ,		_UpperCamelCase     ,		math.exp(_UpperCamelCase ) )
      return log_str

   UpperCamelCase__			    =    ""
   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("=" * 1_00 )
   logger.info(_UpperCamelCase )
   logger.info("=" * 1_00 )


if __name__ == "__main__":
       main()




UpperCAmelCase :				Optional[int]							="""
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""

UpperCAmelCase :				List[Any]							=[{"""type""": """code""", """content""": INSTALL_CONTENT}]
