


'''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
)
__lowercase	:   List[str]   		=					logging.getLogger(__name__)





def 				a_							(    )      ->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       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."     )
				__lowerCamelCase		:   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()

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

				__lowerCamelCase		:   Optional[int]           =       corpus.get_iterator("valid"				,    args.batch_size				,    args.tgt_len				,    device=lowercase				,    ext_len=args.ext_len     )
				__lowerCamelCase		:   List[Any]           =       corpus.get_iterator("test"				,    args.batch_size				,    args.tgt_len				,    device=lowercase				,    ext_len=args.ext_len     )

				# Load a pre-trained model
				__lowerCamelCase		:   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:
								__lowerCamelCase		:   Optional[int]           =       args.clamp_len
				if args.same_length:
								__lowerCamelCase		:   Optional[Any]           =       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(lowercase     ):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								__lowerCamelCase    ,       __lowerCamelCase		:   Optional[int]           =       0, 0.0
								__lowerCamelCase		:   Optional[Any]           =       time.time()
								with torch.no_grad():
												__lowerCamelCase		:   List[str]           =       None
												for idx, (data, target, seq_len) in enumerate(lowercase     ):
																__lowerCamelCase		:   Union[str, Any]           =       model(lowercase				,    lm_labels=lowercase				,    mems=lowercase     )
																__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase		:   Union[str, Any]           =       ret
																__lowerCamelCase		:   Optional[Any]           =       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												__lowerCamelCase		:   List[str]           =       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":
								__lowerCamelCase		:   Optional[int]           =       evaluate(lowercase     )
								__lowerCamelCase		:   List[str]           =       evaluate(lowercase     )
				elif args.split == "valid":
								__lowerCamelCase		:   Tuple           =       evaluate(lowercase     )
								__lowerCamelCase		:   Tuple           =       None
				elif args.split == "test":
								__lowerCamelCase		:   List[Any]           =       evaluate(lowercase     )
								__lowerCamelCase		:   str           =       None

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

				__lowerCamelCase		:   List[str]           =       ""
				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()







from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
