


"""simple docstring"""


import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm






def    a__       ( __lowercase   ,	__lowercase      )		->		Optional[int]:
				if isinstance(__lowercase   ,	(list, tuple)      ):
								return honor_type(__lowercase   ,	(move_to_device(__lowercase   ,	__lowercase      ) for t in state)      )
				elif isinstance(__lowercase   ,	__lowercase      ):
								return type(__lowercase      )({k: move_to_device(__lowercase   ,	__lowercase      ) for k, v in state.items()}      )
				elif isinstance(__lowercase   ,	torch.Tensor      ):
								return state.to(__lowercase      )
				return state




class snake_case					(     torch.optim.Optimizer):







		def __init__(    self					:				str			,      a__					:				List[Any]			,      a__					:				Optional[int]=True			,      a__					:				Dict=None						)				->  Union[str, Any]:





						'''simple docstring'''







						_A         =	optimizer
						_A         =	scaler
						_A         =	AcceleratorState()
						_A         =	GradientState()
						_A         =	device_placement
						_A         =	False
						_A         =	None

						# Handle device placement
						if device_placement:
										_A         =	self.optimizer.state_dict()
										if self.accelerator_state.distributed_type == DistributedType.TPU:
														xm.send_cpu_data_to_device(a__			,      self.accelerator_state.device						)
										else:
														_A         =	move_to_device(a__			,      self.accelerator_state.device						)
										self.optimizer.load_state_dict(a__						)







		@property
		def        a_       (    self					:				Optional[Any]						)				->  Tuple:





						'''simple docstring'''







						return self.optimizer.state







		@state.setter
		def        a_       (    self					:				Dict			,      a__					:				Any						)				->  Dict:





						'''simple docstring'''







						_A         =	state







		@property
		def        a_       (    self					:				List[Any]						)				->  Any:





						'''simple docstring'''







						return self.optimizer.param_groups







		@param_groups.setter
		def        a_       (    self					:				Any			,      a__					:				Tuple						)				->  str:





						'''simple docstring'''







						_A         =	param_groups







		@property
		def        a_       (    self					:				List[str]						)				->  List[Any]:





						'''simple docstring'''







						return self.optimizer.defaults







		@defaults.setter
		def        a_       (    self					:				int			,      a__					:				List[str]						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	defaults







		def        a_       (    self					:				Tuple			,      a__					:				List[Any]						)				->  str:





						'''simple docstring'''







						self.optimizer.add_param_group(a__						)







		def        a_       (    self					:				Any			,      a__					:				Any						)				->  List[str]:





						'''simple docstring'''







						if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
										xm.send_cpu_data_to_device(a__			,      self.accelerator_state.device						)
						self.optimizer.load_state_dict(a__						)







		def        a_       (    self					:				Optional[Any]						)				->  Dict:





						'''simple docstring'''







						return self.optimizer.state_dict()







		def        a_       (    self					:				Any			,      a__					:				List[str]=None						)				->  str:





						'''simple docstring'''







						if self.gradient_state.sync_gradients:
										_A         =	"set_to_none" in inspect.signature(self.optimizer.zero_grad						).parameters
										if accept_arg:
														if set_to_none is None:
																		_A         =	False
														self.optimizer.zero_grad(set_to_none=a__						)
										else:
														if set_to_none is not None:
																		raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."						)
														self.optimizer.zero_grad()







		def        a_       (    self					:				Union[str, Any]			,      a__					:				int=None						)				->  List[Any]:





						'''simple docstring'''







						if self.gradient_state.sync_gradients:
										if self.accelerator_state.distributed_type == DistributedType.TPU:
														_A         =	{"closure": closure} if closure is not None else {}
														xm.optimizer_step(self.optimizer			,      optimizer_args=a__						)
										elif self.scaler is not None:
														_A         =	False
														if self._last_scale is None:
																		# `get_scale` is an async operation requiring full synchronization
																		# on CPU and GPUs before finishing. As a result, we store away
																		# the prior one to reduce the call overhead
																		_A         =	self.scaler.get_scale()
																		_A         =	True
														self.scaler.step(self.optimizer			,      a__						)
														self.scaler.update()
														_A         =	self.scaler.get_scale()
														if not new_scale:
																		# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																		_A         =	scale_after < self._last_scale
														_A         =	scale_after

										else:
														self.optimizer.step(a__						)







		def        a_       (    self					:				Optional[int]			,      a__					:				Dict						)				->  Tuple:





						'''simple docstring'''







						for param_group in self.optimizer.param_groups:
										_A         =	[parameters_map.get(a__			,      a__						) for p in param_group["params"]]







		@property
		def        a_       (    self					:				Optional[int]						)				->  Union[str, Any]:





						'''simple docstring'''







						warnings.warn(
						    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
						    "`optimizer.step_was_skipped` instead."			,      a__			,      )
						return self._is_overflow







		@property
		def        a_       (    self					:				Any						)				->  Any:





						'''simple docstring'''







						return self._is_overflow







		def __getstate__(    self					:				str						)				->  int:





						'''simple docstring'''







						return self.__dict__.copy()







		def __setstate__(    self					:				Union[str, Any]			,      a__					:				str						)				->  Any:





						'''simple docstring'''







						self.__dict__.update(a__						)




"""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     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_lowerCamelCase					     = 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=1_28  ,      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=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      default=10_00  ,      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					     = 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					     = 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					     = TransfoXLCorpus.from_pretrained(args.model_name							)

				_lowerCamelCase					     = corpus.get_iterator('''valid'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)
				_lowerCamelCase					     = corpus.get_iterator('''test'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)

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

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(lowercase_   :							Any							):
								# 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(lowercase_							):
																_lowerCamelCase					     = model(lowercase_  ,      lm_labels=lowercase_  ,      mems=lowercase_							)
																_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(lowercase_  ,      10_00 * total_time / (idx + 1)							)							)
								return total_loss / total_len

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

				def format_log(lowercase_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							):
								_lowerCamelCase					     = '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(lowercase_  ,      lowercase_  ,      math.exp(lowercase_							)							)
								return log_str

				_lowerCamelCase					     = ''''''
				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('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
