

'''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     SCREAMING_SNAKE_CASE(	)     ->     Any:
	UpperCAmelCase_		:   int													=       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=1_0 ,help='batch size'	)
	parser.add_argument('--tgt_len' ,type=_lowerCamelCase ,default=1_2_8 ,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=1_6_0_0 ,help='length of the retained previous heads'	)
	parser.add_argument('--clamp_len' ,type=_lowerCamelCase ,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=_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.'	)
	UpperCAmelCase_		:   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=_lowerCamelCase	)
		ptvsd.wait_for_attach()

	UpperCAmelCase_		:   Optional[Any]													=       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 )
	UpperCAmelCase_		:   Optional[Any]													=       TransfoXLCorpus.from_pretrained(args.model_name	)

	UpperCAmelCase_		:   int													=       corpus.get_iterator('valid' ,args.batch_size ,args.tgt_len ,device=_lowerCamelCase ,ext_len=args.ext_len	)
	UpperCAmelCase_		:   Optional[Any]													=       corpus.get_iterator('test' ,args.batch_size ,args.tgt_len ,device=_lowerCamelCase ,ext_len=args.ext_len	)

	# Load a pre-trained model
	UpperCAmelCase_		:   Any													=       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:
		UpperCAmelCase_		:   Union[str, Any]													=       args.clamp_len
	if args.same_length:
		UpperCAmelCase_		:   Optional[Any]													=       True

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

	# Run on test data.
	if args.split == "all":
		UpperCAmelCase_		:   str													=       evaluate(_lowerCamelCase	)
		UpperCAmelCase_		:   Any													=       evaluate(_lowerCamelCase	)
	elif args.split == "valid":
		UpperCAmelCase_		:   Dict													=       evaluate(_lowerCamelCase	)
		UpperCAmelCase_		:   List[Any]													=       None
	elif args.split == "test":
		UpperCAmelCase_		:   Optional[Any]													=       evaluate(_lowerCamelCase	)
		UpperCAmelCase_		:   List[Any]													=       None

	def format_log(UpperCamelCase ,UpperCamelCase	):
		UpperCAmelCase_		:   Any													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_lowerCamelCase ,_lowerCamelCase ,math.exp(_lowerCamelCase	)	)
		return log_str

	UpperCAmelCase_		:   Tuple													=       ''
	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_0_0	)
	logger.info(_lowerCamelCase	)
	logger.info('=' * 1_0_0	)


if __name__ == "__main__":
						main()














"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

