



'''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
)
snake_case_				:			Tuple						 =						logging.getLogger(__name__)






def A__					(  ):
						_UpperCamelCase      :	str				       =     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=1_0						,						help='batch size'							)
						parser.add_argument('--tgt_len'						,						type=UpperCamelCase_						,						default=1_2_8						,						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_6_0_0						,						help='length of the retained previous heads'							)
						parser.add_argument('--clamp_len'						,						type=UpperCamelCase_						,						default=1_0_0_0						,						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.'							)
						_UpperCamelCase      :	Optional[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()

						_UpperCamelCase      :	int				       =     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 )
						_UpperCamelCase      :	Any				       =     TransfoXLCorpus.from_pretrained(args.model_name							)

						_UpperCamelCase      :	List[Any]				       =     corpus.get_iterator('valid'						,						args.batch_size						,						args.tgt_len						,						device=UpperCamelCase_						,						ext_len=args.ext_len							)
						_UpperCamelCase      :	Any				       =     corpus.get_iterator('test'						,						args.batch_size						,						args.tgt_len						,						device=UpperCamelCase_						,						ext_len=args.ext_len							)

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

						###############################################################################
						# Evaluation code
						###############################################################################
						def evaluate(UpperCAmelCase_							):
												# Turn on evaluation mode which disables dropout.
												model.eval()
												_UpperCamelCase       ,    _UpperCamelCase      :	Tuple				       =     0, 0.0
												_UpperCamelCase      :	List[Any]				       =     time.time()
												with torch.no_grad():
																		_UpperCamelCase      :	List[str]				       =     None
																		for idx, (data, target, seq_len) in enumerate(UpperCamelCase_							):
																								_UpperCamelCase      :	Optional[int]				       =     model(UpperCamelCase_						,						lm_labels=UpperCamelCase_						,						mems=UpperCamelCase_							)
																								_UpperCamelCase       ,    _UpperCamelCase       ,    _UpperCamelCase      :	Optional[Any]				       =     ret
																								_UpperCamelCase      :	Optional[Any]				       =     loss.mean()
																								total_loss += seq_len * loss.item()
																								total_len += seq_len
																		_UpperCamelCase      :	List[Any]				       =     time.time() - start_time
												logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(UpperCamelCase_						,						1_0_0_0 * total_time / (idx + 1)							)							)
												return total_loss / total_len

						# Run on test data.
						if args.split == "all":
												_UpperCamelCase      :	Tuple				       =     evaluate(UpperCamelCase_							)
												_UpperCamelCase      :	List[Any]				       =     evaluate(UpperCamelCase_							)
						elif args.split == "valid":
												_UpperCamelCase      :	str				       =     evaluate(UpperCamelCase_							)
												_UpperCamelCase      :	str				       =     None
						elif args.split == "test":
												_UpperCamelCase      :	List[Any]				       =     evaluate(UpperCamelCase_							)
												_UpperCamelCase      :	List[str]				       =     None

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

						_UpperCamelCase      :	Union[str, 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('=' * 1_0_0							)
						logger.info(UpperCamelCase_							)
						logger.info('=' * 1_0_0							)


if __name__ == "__main__":
					main()







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     torch.cuda.device_count()
						print(f'Device count: {num_processes}'							)
						with patch_environment(
						    world_size=UpperCAmelCase_						,						master_addr='127.0.01'						,						master_port='29500'						,						mixed_precision=args[1].mixed_precision							):
												_UpperCamelCase      :	Optional[int]				       =     PrepareForLaunch(UpperCAmelCase_						,						distributed_type='MULTI_GPU'							)
												torch.multiprocessing.start_processes(UpperCAmelCase_						,						args=UpperCAmelCase_						,						nprocs=UpperCAmelCase_						,						start_method='spawn'							)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					snake_case_				:			Optional[Any]						 =						rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					snake_case_				:			List[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					snake_case_				:			List[str]						 =						argparse.Namespace(cpu=False, mixed_precision='fp16')
					snake_case_				:			List[str]						 =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])


