"""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						=    logging.getLogger(__name__)


def   __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase			:     Union[str, Any]      					=				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=1_600  ,help='length of the retained previous heads'      )
	parser.add_argument('--clamp_len'  ,type=__UpperCAmelCase  ,default=1_000  ,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.'      )
	_lowercase			:     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=__UpperCAmelCase      )
		ptvsd.wait_for_attach()

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

	_lowercase			:     Optional[Any]      					=				corpus.get_iterator('valid'  ,args.batch_size  ,args.tgt_len  ,device=__UpperCAmelCase  ,ext_len=args.ext_len      )
	_lowercase			:     List[str]      					=				corpus.get_iterator('test'  ,args.batch_size  ,args.tgt_len  ,device=__UpperCAmelCase  ,ext_len=args.ext_len      )

	# Load a pre-trained model
	_lowercase			:     Any      					=				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:
		_lowercase			:     List[Any]      					=				args.clamp_len
	if args.same_length:
		_lowercase			:     List[str]      					=				True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(__UpperCAmelCase      ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		_lowercase					,   _lowercase			:     Union[str, Any]      					=				0, 0.0
		_lowercase			:     Optional[Any]      					=				time.time()
		with torch.no_grad():
			_lowercase			:     Union[str, Any]      					=				None
			for idx, (data, target, seq_len) in enumerate(__UpperCAmelCase      ):
				_lowercase			:     Any      					=				model(__UpperCAmelCase  ,lm_labels=__UpperCAmelCase  ,mems=__UpperCAmelCase      )
				_lowercase					,   _lowercase					,   _lowercase			:     Optional[int]      					=				ret
				_lowercase			:     List[str]      					=				loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			_lowercase			:     Optional[int]      					=				time.time() - start_time
		logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(__UpperCAmelCase  ,1_000 * total_time / (idx + 1)      )      )
		return total_loss / total_len

	# Run on test data.
	if args.split == "all":
		_lowercase			:     int      					=				evaluate(__UpperCAmelCase      )
		_lowercase			:     Optional[Any]      					=				evaluate(__UpperCAmelCase      )
	elif args.split == "valid":
		_lowercase			:     int      					=				evaluate(__UpperCAmelCase      )
		_lowercase			:     Dict      					=				None
	elif args.split == "test":
		_lowercase			:     Optional[Any]      					=				evaluate(__UpperCAmelCase      )
		_lowercase			:     Union[str, Any]      					=				None

	def format_log(__UpperCAmelCase  ,__UpperCAmelCase      ):
		_lowercase			:     str      					=				'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(__UpperCAmelCase  ,__UpperCAmelCase  ,math.exp(__UpperCAmelCase      )      )
		return log_str

	_lowercase			:     Any      					=				''
	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 __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE						=    TypeVar('T', bound=bool)



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     T      					=				value
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None







				@property
				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					return self._value







				@staticmethod
				def 				__UpperCAmelCase       (			lowerCamelCase_     :							RandomizedHeapNode[T] | None      ,       lowerCamelCase_     :							RandomizedHeapNode[T] | None    ):

					"""simple docstring"""






					if not roota:
						return roota

					if not roota:
						return roota

					if roota.value > roota.value:
						_lowercase					,   _lowercase			:     List[Any]      					=				roota, roota

					if random.choice([True, False]    ):
						_lowercase					,   _lowercase			:     Optional[int]      					=				roota.right, roota.left

					_lowercase			:     Tuple      					=				RandomizedHeapNode.merge(roota.left      ,       lowerCamelCase_    )

					return roota



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							Iterable[T] | None = ()    ):

					"""simple docstring"""






					_lowercase			:     RandomizedHeapNode[T] | None      					=				None

					if data:
						for item in data:
							self.insert(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     str      					=				RandomizedHeapNode.merge(self._root      ,       RandomizedHeapNode(lowerCamelCase_    )    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.top()

					if self._root is None:
						return None

					_lowercase			:     List[Any]      					=				RandomizedHeapNode.merge(self._root.left      ,       self._root.right    )

					return result







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					if not self._root:
						raise IndexError('Can\'t get top element for the empty heap.'    )
					return self._root.value







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				None







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				[]
					while self:
						result.append(self.pop()    )

					return result







				def __bool__(			self     :							List[str]    ):

					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()



