




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      :				Tuple				      =logging.getLogger(__name__)

def      lowerCAmelCase_     (					)   ->     List[Any]:


 '''simple docstring'''




 lowerCamelCase__ 					=			argparse.ArgumentParser(description='PyTorch Transformer Language Model'  )
 parser.add_argument('--model_name'       ,			type=lowerCamelCase       ,			default='transfo-xl-wt103'       ,			help='pretrained model name'  )
 parser.add_argument(
     '--split'       ,			type=lowerCamelCase       ,			default='test'       ,			choices=['all', 'valid', 'test']       ,			help='which split to evaluate'  )
 parser.add_argument('--batch_size'       ,			type=lowerCamelCase       ,			default=10       ,			help='batch size'  )
 parser.add_argument('--tgt_len'       ,			type=lowerCamelCase       ,			default=1_28       ,			help='number of tokens to predict'  )
 parser.add_argument('--ext_len'       ,			type=lowerCamelCase       ,			default=0       ,			help='length of the extended context'  )
 parser.add_argument('--mem_len'       ,			type=lowerCamelCase       ,			default=16_00       ,			help='length of the retained previous heads'  )
 parser.add_argument('--clamp_len'       ,			type=lowerCamelCase       ,			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=lowerCamelCase       ,			required=lowerCamelCase       ,			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=lowerCamelCase       ,			default=''       ,			help='Can be used for distant debugging.'  )
 parser.add_argument('--server_port'       ,			type=lowerCamelCase       ,			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=lowerCamelCase  )
  ptvsd.wait_for_attach()

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

 # 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=lowerCamelCase       ,			ext_len=args.ext_len  )
 lowerCamelCase__ 					=			corpus.get_iterator('test'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowerCamelCase       ,			ext_len=args.ext_len  )

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

 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(lowerCamelCase:    Union[str, Any]  ):
  # 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(lowerCamelCase  ):
    lowerCamelCase__ 					=			model(lowerCamelCase       ,			lm_labels=lowerCamelCase       ,			mems=lowerCamelCase  )
    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(lowerCamelCase       ,			10_00 * total_time / (idx + 1)  )  )
  return total_loss / total_len

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

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

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

 logger.info('=' * 1_00  )
 logger.info(lowerCamelCase  )
 logger.info('=' * 1_00  )


if __name__ == "__main__":
      main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)