



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
)
__snake_case		:List[Any] 						=	logging.getLogger(__name__)





def 	__snake_case      (     ):
    __a					=							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.'''							)
    __a					=							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()

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

    __a					=							corpus.get_iterator('''valid'''  ,					args.batch_size  ,					args.tgt_len  ,					device=_UpperCAmelCase  ,					ext_len=args.ext_len							)
    __a					=							corpus.get_iterator('''test'''  ,					args.batch_size  ,					args.tgt_len  ,					device=_UpperCAmelCase  ,					ext_len=args.ext_len							)

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

    ###############################################################################
    # Evaluation code
    ###############################################################################
    def evaluate(_UpperCAmelCase							):
        # Turn on evaluation mode which disables dropout.
        model.eval()
        __a			,  __a					=							0, 0.0
        __a					=							time.time()
        with torch.no_grad():
            __a					=							None
            for idx, (data, target, seq_len) in enumerate(_UpperCAmelCase							):
                __a					=							model(_UpperCAmelCase  ,					lm_labels=_UpperCAmelCase  ,					mems=_UpperCAmelCase							)
                __a			,  __a			,  __a					=							ret
                __a					=							loss.mean()
                total_loss += seq_len * loss.item()
                total_len += seq_len
            __a					=							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":
        __a					=							evaluate(_UpperCAmelCase							)
        __a					=							evaluate(_UpperCAmelCase							)
    elif args.split == "valid":
        __a					=							evaluate(_UpperCAmelCase							)
        __a					=							None
    elif args.split == "test":
        __a					=							evaluate(_UpperCAmelCase							)
        __a					=							None

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

    __a					=							''''''
    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()










from __future__ import annotations

class        _A			:



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	int = 0):


           '''simple docstring'''

           __a					=							value
           __a					=							None
           __a					=							None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase							):
    if treea is None:
        return treea
    if treea is None:
        return treea

    __a					=							treea.value + treea.value
    __a					=							merge_two_binary_trees(treea.left  ,					treea.left							)
    __a					=							merge_two_binary_trees(treea.right  ,					treea.right							)
    return treea





def 	__snake_case      (     _UpperCAmelCase							):
    if root:
        print(root.value							)
        print_preorder(root.left							)
        print_preorder(root.right							)


if __name__ == "__main__":
 __snake_case		:List[Any] 						=	Node(1)
 __snake_case		:Optional[Any] 						=	Node(2)
 __snake_case		:Dict 						=	Node(3)
 __snake_case		:int 						=	Node(4)

 __snake_case		:List[str] 						=	Node(2)
 __snake_case		:List[str] 						=	Node(4)
 __snake_case		:Tuple 						=	Node(6)
 __snake_case		:str 						=	Node(9)
 __snake_case		:List[str] 						=	Node(5)

 print('''Tree1 is: ''')
 print_preorder(treea)
 print('''Tree2 is: ''')
 print_preorder(treea)
 __snake_case		:Union[str, Any] 						=	merge_two_binary_trees(treea, treea)
 print('''Merged Tree is: ''')
 print_preorder(merged_tree)





