"""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
)
a							=logging.getLogger(__name__)





def       lowerCamelCase_			( )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ =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=1600       ,   help="length of the retained previous heads"    )
						parser.add_argument("--clamp_len"       ,   type=__lowerCAmelCase       ,   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=__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."    )
						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=__lowerCAmelCase    )
												ptvsd.wait_for_attach()

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

						lowerCamelCase__ =corpus.get_iterator("valid"       ,   args.batch_size       ,   args.tgt_len       ,   device=__lowerCAmelCase       ,   ext_len=args.ext_len    )
						lowerCamelCase__ =corpus.get_iterator("test"       ,   args.batch_size       ,   args.tgt_len       ,   device=__lowerCAmelCase       ,   ext_len=args.ext_len    )

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

						###############################################################################
						# Evaluation code
						###############################################################################
						def evaluate(__lowerCAmelCase    ):
												# 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(__lowerCAmelCase    ):
																								lowerCamelCase__ =model(__lowerCAmelCase       ,   lm_labels=__lowerCAmelCase       ,   mems=__lowerCAmelCase    )
																								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(__lowerCAmelCase       ,   1000 * total_time / (idx + 1)    )    )
												return total_loss / total_len

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

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

						lowerCamelCase__ =""
						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





def       lowerCamelCase_			( __lowerCAmelCase    )							-> int | float:




						'''simple docstring'''



						lowerCamelCase__ =sorted(__lowerCAmelCase    )
						lowerCamelCase__ =len(__lowerCAmelCase    )
						lowerCamelCase__ =length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


