

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 	snake_case_      ()			->	Dict:
   __lowerCAmelCase  :			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=1_0					,   help="""batch size"""     )
   parser.add_argument("""--tgt_len"""					,   type=__A					,   default=1_2_8					,   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_6_0_0					,   help="""length of the retained previous heads"""     )
   parser.add_argument("""--clamp_len"""					,   type=__A					,   default=1_0_0_0					,   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."""     )
   __lowerCAmelCase  :			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()

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

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

   # Load a pre-trained model
   __lowerCAmelCase  :			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:
      __lowerCAmelCase  :			Optional[int]					    =						args.clamp_len
   if args.same_length:
      __lowerCAmelCase  :			int					    =						True

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(__A     :						Union[str, Any]     ):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      __lowerCAmelCase    ,__lowerCAmelCase  :			Optional[Any]					    =						0, 0.0
      __lowerCAmelCase  :			List[Any]					    =						time.time()
      with torch.no_grad():
         __lowerCAmelCase  :			Dict					    =						None
         for idx, (data, target, seq_len) in enumerate(__A     ):
            __lowerCAmelCase  :			Any					    =						model(__A					,   lm_labels=__A					,   mems=__A     )
            __lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase  :			Optional[int]					    =						ret
            __lowerCAmelCase  :			Optional[int]					    =						loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         __lowerCAmelCase  :			Union[str, Any]					    =						time.time() - start_time
      logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__A					,   1_0_0_0 * total_time / (idx + 1)     )     )
      return total_loss / total_len

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

   def format_log(__A     :						Dict					,   __A     :						Union[str, Any]     ):
      __lowerCAmelCase  :			int					    =						"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__A					,   __A					,   math.exp(__A     )     )
      return log_str

   __lowerCAmelCase  :			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("""=""" * 1_0_0     )
   logger.info(__A     )
   logger.info("""=""" * 1_0_0     )


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    )


