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 SCREAMING_SNAKE_CASE					(  ):
       _UpperCamelCase  :							Optional[int]	    =	argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
       parser.add_argument('--model_name'		,    type=lowercase_		,    default='transfo-xl-wt103'		,    help='pretrained model name'					)
       parser.add_argument(
           '--split'		,    type=lowercase_		,    default='test'		,    choices=['all', 'valid', 'test']		,    help='which split to evaluate'					)
       parser.add_argument('--batch_size'		,    type=lowercase_		,    default=10		,    help='batch size'					)
       parser.add_argument('--tgt_len'		,    type=lowercase_		,    default=128		,    help='number of tokens to predict'					)
       parser.add_argument('--ext_len'		,    type=lowercase_		,    default=0		,    help='length of the extended context'					)
       parser.add_argument('--mem_len'		,    type=lowercase_		,    default=1600		,    help='length of the retained previous heads'					)
       parser.add_argument('--clamp_len'		,    type=lowercase_		,    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=lowercase_		,    required=lowercase_		,    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=lowercase_		,    default=''		,    help='Can be used for distant debugging.'					)
       parser.add_argument('--server_port'		,    type=lowercase_		,    default=''		,    help='Can be used for distant debugging.'					)
       _UpperCamelCase  :							List[Any]	    =	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=lowercase_					)
              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(lowercase_					)					)

       # 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  :							List[str]	    =	TransfoXLCorpus.from_pretrained(args.model_name					)

       _UpperCamelCase  :							Union[str, Any]	    =	corpus.get_iterator('valid'		,    args.batch_size		,    args.tgt_len		,    device=lowercase_		,    ext_len=args.ext_len					)
       _UpperCamelCase  :							Dict	    =	corpus.get_iterator('test'		,    args.batch_size		,    args.tgt_len		,    device=lowercase_		,    ext_len=args.ext_len					)

       # Load a pre-trained model
       _UpperCamelCase  :							Optional[Any]	    =	TransfoXLLMHeadModel.from_pretrained(args.model_name					)
       model.to(lowercase_					)

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

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(lowercase_:						Tuple					):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	0, 0.0
              _UpperCamelCase  :							Any	    =	time.time()
              with torch.no_grad():
                     _UpperCamelCase  :							Optional[int]	    =	None
                     for idx, (data, target, seq_len) in enumerate(lowercase_					):
                            _UpperCamelCase  :							List[str]	    =	model(lowercase_		,    lm_labels=lowercase_		,    mems=lowercase_					)
                            _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	ret
                            _UpperCamelCase  :							Optional[int]	    =	loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     _UpperCamelCase  :							Dict	    =	time.time() - start_time
              logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(lowercase_		,    1000 * total_time / (idx + 1)					)					)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              _UpperCamelCase  :							str	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							Tuple	    =	evaluate(lowercase_					)
       elif args.split == "valid":
              _UpperCamelCase  :							str	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							str	    =	None
       elif args.split == "test":
              _UpperCamelCase  :							int	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							Dict	    =	None

       def format_log(lowercase_:						Dict		,    lowercase_:						List[str]					):
              _UpperCamelCase  :							Union[str, Any]	    =	'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase_		,    lowercase_		,    math.exp(lowercase_					)					)
              return log_str

       _UpperCamelCase  :							Optional[Any]	    =	''
       if valid_loss is not None:
              log_str += format_log(lowercase_		,    'valid'					)
       if test_loss is not None:
              log_str += format_log(lowercase_		,    'test'					)

       logger.info('=' * 100					)
       logger.info(lowercase_					)
       logger.info('=' * 100					)


if __name__ == "__main__":
 main()









"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





