


'''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
)
a_  :    Tuple      =			logging.getLogger(__name__)





def __snake_case (				):
	lowerCamelCase_								=  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=128 ,    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=1600 ,    help="length of the retained previous heads"		)
	parser.add_argument("--clamp_len" ,    type=UpperCAmelCase_ ,    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=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."		)
	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=UpperCAmelCase_		)
		ptvsd.wait_for_attach()

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

	lowerCamelCase_								=  corpus.get_iterator("valid" ,    args.batch_size ,    args.tgt_len ,    device=UpperCAmelCase_ ,    ext_len=args.ext_len		)
	lowerCamelCase_								=  corpus.get_iterator("test" ,    args.batch_size ,    args.tgt_len ,    device=UpperCAmelCase_ ,    ext_len=args.ext_len		)

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

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(UpperCAmelCase_						:						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(UpperCAmelCase_		):
				lowerCamelCase_								=  model(UpperCAmelCase_ ,    lm_labels=UpperCAmelCase_ ,    mems=UpperCAmelCase_		)
				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(UpperCAmelCase_ ,    1000 * total_time / (idx + 1)		)		)
		return total_loss / total_len

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

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

	lowerCamelCase_								=  ""
	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("=" * 100		)
	logger.info(UpperCAmelCase_		)
	logger.info("=" * 100		)


if __name__ == "__main__":
					main()







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_  :    Optional[Any]      =			logging.get_logger(__name__)

a_  :    Tuple      =			{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "electra"






  def __init__(   self      ,  UpperCamelCase=3_0522      ,  UpperCamelCase=128      ,  UpperCamelCase=256      ,  UpperCamelCase=12      ,  UpperCamelCase=4      ,  UpperCamelCase=1024      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=512      ,  UpperCamelCase=2      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-12      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0      ,  UpperCamelCase="absolute"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase      ,  **UpperCamelCase					)

   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  embedding_size
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  layer_norm_eps

   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_last_dropout
   lowerCamelCase_								=  position_embedding_type
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  classifier_dropout





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_								=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    lowerCamelCase_								=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ]					)



