
"""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       lowercase__  (   )	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   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=10	,			help='batch size'	)
    parser.add_argument('--tgt_len'	,			type=lowerCAmelCase	,			default=128	,			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_600	,			help='length of the retained previous heads'	)
    parser.add_argument('--clamp_len'	,			type=lowerCAmelCase	,			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=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       =   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       =   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       =   TransfoXLCorpus.from_pretrained(args.model_name	)

    UpperCAmelCase       =   corpus.get_iterator('valid'	,			args.batch_size	,			args.tgt_len	,			device=lowerCAmelCase	,			ext_len=args.ext_len	)
    UpperCAmelCase       =   corpus.get_iterator('test'	,			args.batch_size	,			args.tgt_len	,			device=lowerCAmelCase	,			ext_len=args.ext_len	)

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

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

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

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

    UpperCAmelCase       =   ''
    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('=' * 100	)
    logger.info(lowerCAmelCase	)
    logger.info('=' * 100	)


if __name__ == "__main__":
   main()





"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_         =  '''Alexander Joslin'''




def       lowercase__  (   lowerCAmelCase			:   int	)	-> int:






    """simple docstring"""




    if number <= 0:
        UpperCAmelCase       =   F"n must be greater than 0. Got n = {number}"
        raise ValueError(lowerCAmelCase	)

    UpperCAmelCase       =   [number + 1] * (number + 1)

    # starting position
    UpperCAmelCase       =   0
    for i in range(1	,			lowerCAmelCase	):
        UpperCAmelCase       =   min(table[i + 1]	,			table[i] + 1	)
        # check if out of bounds
        if i * 2 <= number:
            UpperCAmelCase       =   min(table[i * 2]	,			table[i] + 1	)
        # check if out of bounds
        if i * 3 <= number:
            UpperCAmelCase       =   min(table[i * 3]	,			table[i] + 1	)
    return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



