


'''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
)
__UpperCAmelCase			:Optional[Any] 				=   logging.getLogger(__name__)




def 	_a	(						):





     '''simple docstring'''






     __UpperCAmelCase	: 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.'''  )
     __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=_lowercase  )
          ptvsd.wait_for_attach()

     __UpperCAmelCase	: Any  							=			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 )
     __UpperCAmelCase	: List[Any]  							=			TransfoXLCorpus.from_pretrained(args.model_name  )

     __UpperCAmelCase	: List[str]  							=			corpus.get_iterator('''valid'''			,					args.batch_size			,					args.tgt_len			,					device=_lowercase			,					ext_len=args.ext_len  )
     __UpperCAmelCase	: Dict  							=			corpus.get_iterator('''test'''			,					args.batch_size			,					args.tgt_len			,					device=_lowercase			,					ext_len=args.ext_len  )

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

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

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

     __UpperCAmelCase	: 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()

'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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







def 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			torch.tensor(xm.get_rng_state())
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(A)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(A)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(A)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(A)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item())
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(A)







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




