

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       snake_case_(_UpperCamelCase ,       _UpperCamelCase = False     )							->	Optional[int]:



  """simple docstring"""






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



def       snake_case_(_UpperCamelCase = None ,       _UpperCamelCase = None     )							->	List[Any]:



  """simple docstring"""






  if rng_type == RNGType.TORCH:
    _snake_case						=							torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    _snake_case						=							torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    _snake_case						=							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."
    _snake_case						=							torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    _snake_case						=							torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    _snake_case						=							generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  _snake_case						=							AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    _snake_case						=							rng_state.to(xm.xla_device()     )
    xm.collective_broadcast([rng_state]     )
    xm.mark_step()
    _snake_case						=							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
  ):
    _snake_case						=							rng_state.to(state.device     )
    torch.distributed.broadcast(_UpperCamelCase ,       0     )
    _snake_case						=							rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(_UpperCamelCase ,       0     )

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



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase = None     )							->	Optional[int]:



  """simple docstring"""






  for rng_type in rng_types:
    synchronize_rng_state(RNGType(_UpperCamelCase     ) ,       generator=_UpperCamelCase     )



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







def 						A_(			):
							UpperCamelCase					       =			argparse.ArgumentParser(description='PyTorch Transformer Language Model')
							parser.add_argument('--model_name'						,			type=A						,			default='transfo-xl-wt103'						,			help='pretrained model name')
							parser.add_argument(
							    '--split'						,			type=A						,			default='test'						,			choices=['all', 'valid', 'test']						,			help='which split to evaluate')
							parser.add_argument('--batch_size'						,			type=A						,			default=10						,			help='batch size')
							parser.add_argument('--tgt_len'						,			type=A						,			default=128						,			help='number of tokens to predict')
							parser.add_argument('--ext_len'						,			type=A						,			default=0						,			help='length of the extended context')
							parser.add_argument('--mem_len'						,			type=A						,			default=1600						,			help='length of the retained previous heads')
							parser.add_argument('--clamp_len'						,			type=A						,			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=A						,			required=A						,			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=A						,			default=''						,			help='Can be used for distant debugging.')
							parser.add_argument('--server_port'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							UpperCamelCase					       =			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=A)
														ptvsd.wait_for_attach()

							UpperCamelCase					       =			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
							logger.info('device: {}'.format(A))

							# 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					       =			TransfoXLCorpus.from_pretrained(args.model_name)

							UpperCamelCase					       =			corpus.get_iterator('valid'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)
							UpperCamelCase					       =			corpus.get_iterator('test'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)

							# Load a pre-trained model
							UpperCamelCase					       =			TransfoXLLMHeadModel.from_pretrained(args.model_name)
							model.to(A)

							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					       =			args.clamp_len
							if args.same_length:
														UpperCamelCase					       =			True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(A		:						Dict):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														UpperCamelCase       ,     UpperCamelCase					       =			0, 0.0
														UpperCamelCase					       =			time.time()
														with torch.no_grad():
																					UpperCamelCase					       =			None
																					for idx, (data, target, seq_len) in enumerate(A):
																												UpperCamelCase					       =			model(A						,			lm_labels=A						,			mems=A)
																												UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			ret
																												UpperCamelCase					       =			loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					UpperCamelCase					       =			time.time() - start_time
														logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A						,			1000 * total_time / (idx + 1)))
														return total_loss / total_len

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

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

							UpperCamelCase					       =			''
							if valid_loss is not None:
														log_str += format_log(A						,			'valid')
							if test_loss is not None:
														log_str += format_log(A						,			'test')

							logger.info('=' * 100)
							logger.info(A)
							logger.info('=' * 100)


if __name__ == "__main__":
						main()




