






"""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
)
_SCREAMING_SNAKE_CASE :    List[str]    =						logging.getLogger(__name__)







def 					lowerCamelCase__	(							)				->    int:
		lowerCamelCase_   					=						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=1600		,    help='length of the retained previous heads'			)
		parser.add_argument('--clamp_len'		,    type=_A		,    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=_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_   					=						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_   					=						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_   					=						TransfoXLCorpus.from_pretrained(args.model_name			)

		lowerCamelCase_   					=						corpus.get_iterator('valid'		,    args.batch_size		,    args.tgt_len		,    device=_A		,    ext_len=args.ext_len			)
		lowerCamelCase_   					=						corpus.get_iterator('test'		,    args.batch_size		,    args.tgt_len		,    device=_A		,    ext_len=args.ext_len			)

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

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(_lowerCamelCase	:		Optional[int]			):
				# 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(_A			):
								lowerCamelCase_   					=						model(_A		,    lm_labels=_A		,    mems=_A			)
								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(_A		,    1000 * total_time / (idx + 1)			)			)
				return total_loss / total_len

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

		def format_log(_lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Any			):
				lowerCamelCase_   					=						'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A		,    _A		,    math.exp(_A			)			)
				return log_str

		lowerCamelCase_   					=						''
		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()












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'''Binary tree: {binary_tree}''')
  _SCREAMING_SNAKE_CASE :    Dict    =						binary_tree_mirror(binary_tree, 5)
  print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




