





"""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
)
lowerCamelCase__      =      logging.getLogger(__name__)




def     lowercase__			(    )    ->						Dict:




      """simple docstring"""

      _UpperCamelCase					:		Optional[Any]  =				argparse.ArgumentParser(description="PyTorch Transformer Language Model"	)
      parser.add_argument("--model_name"						,type=__A						,default="transfo-xl-wt103"						,help="pretrained model name"	)
      parser.add_argument(
          "--split"						,type=__A						,default="test"						,choices=["all", "valid", "test"]						,help="which split to evaluate"	)
      parser.add_argument("--batch_size"						,type=__A						,default=10						,help="batch size"	)
      parser.add_argument("--tgt_len"						,type=__A						,default=128						,help="number of tokens to predict"	)
      parser.add_argument("--ext_len"						,type=__A						,default=0						,help="length of the extended context"	)
      parser.add_argument("--mem_len"						,type=__A						,default=1_600						,help="length of the retained previous heads"	)
      parser.add_argument("--clamp_len"						,type=__A						,default=1_000						,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=__A						,required=__A						,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=__A						,default=""						,help="Can be used for distant debugging."	)
      parser.add_argument("--server_port"						,type=__A						,default=""						,help="Can be used for distant debugging."	)
      _UpperCamelCase					:		List[str]  =				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=__A	)
            ptvsd.wait_for_attach()

      _UpperCamelCase					:		List[Any]  =				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)
      logger.info("device: {}".format(__A	)	)

      # 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					:		str  =				TransfoXLCorpus.from_pretrained(args.model_name	)

      _UpperCamelCase					:		List[Any]  =				corpus.get_iterator("valid"						,args.batch_size						,args.tgt_len						,device=__A						,ext_len=args.ext_len	)
      _UpperCamelCase					:		Optional[int]  =				corpus.get_iterator("test"						,args.batch_size						,args.tgt_len						,device=__A						,ext_len=args.ext_len	)

      # Load a pre-trained model
      _UpperCamelCase					:		List[str]  =				TransfoXLLMHeadModel.from_pretrained(args.model_name	)
      model.to(__A	)

      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					:		Optional[int]  =				args.clamp_len
      if args.same_length:
            _UpperCamelCase					:		int  =				True

      ###############################################################################
      # Evaluation code
      ###############################################################################
      def evaluate(lowercase_	):
            # Turn on evaluation mode which disables dropout.
            model.eval()
            _UpperCamelCase					:		Optional[Any]  =				0, 0.0
            _UpperCamelCase					:		List[Any]  =				time.time()
            with torch.no_grad():
                  _UpperCamelCase					:		Dict  =				None
                  for idx, (data, target, seq_len) in enumerate(__A	):
                        _UpperCamelCase					:		Any  =				model(__A						,lm_labels=__A						,mems=__A	)
                        _UpperCamelCase					:		Optional[int]  =				ret
                        _UpperCamelCase					:		Optional[int]  =				loss.mean()
                        total_loss += seq_len * loss.item()
                        total_len += seq_len
                  _UpperCamelCase					:		Union[str, Any]  =				time.time() - start_time
            logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(__A						,1_000 * total_time / (idx + 1)	)	)
            return total_loss / total_len

      # Run on test data.
      if args.split == "all":
            _UpperCamelCase					:		List[Any]  =				evaluate(__A	)
            _UpperCamelCase					:		Union[str, Any]  =				evaluate(__A	)
      elif args.split == "valid":
            _UpperCamelCase					:		Any  =				evaluate(__A	)
            _UpperCamelCase					:		Any  =				None
      elif args.split == "test":
            _UpperCamelCase					:		Dict  =				evaluate(__A	)
            _UpperCamelCase					:		List[str]  =				None

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

      _UpperCamelCase					:		Tuple  =				""""""
      if valid_loss is not None:
            log_str += format_log(__A						,"valid"	)
      if test_loss is not None:
            log_str += format_log(__A						,"test"	)

      logger.info("=" * 100	)
      logger.info(__A	)
      logger.info("=" * 100	)


if __name__ == "__main__":
  main()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


