import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
      import tensorflow as tf
      from tensorflow.python.framework.errors_impl import ResourceExhaustedError

      from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
      import pyanvml.pyanvml as nvml

UpperCAmelCase_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       def run_func(lowerCAmelCase_   ):
              @wraps(lowerCAmelCase_   )
              def run_in_eager_mode(*lowerCAmelCase_       ,					**lowerCAmelCase_   ):
                     return func(*lowerCAmelCase_       ,					**lowerCAmelCase_   )

              @wraps(lowerCAmelCase_   )
              @tf.function(experimental_compile=lowerCAmelCase_   )
              def run_in_graph_mode(*lowerCAmelCase_       ,					**lowerCAmelCase_   ):
                     return func(*lowerCAmelCase_       ,					**lowerCAmelCase_   )

              if do_eager_mode is True:
                     if use_xla is not False:
                            raise ValueError(
                                'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'   )
                     return run_in_eager_mode
              else:
                     return run_in_graph_mode

       return run_func

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						["tf.Tensor"]:




       '''simple docstring'''






       _A=	random.Random()
       _A=	[rng.randint(0       ,					vocab_size - 1   ) for i in range(batch_size * sequence_length   )]
       return tf.constant(lowerCAmelCase_       ,					shape=(batch_size, sequence_length)       ,					dtype=tf.intaa   )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     TensorFlowBenchmarkArguments
     _SCREAMING_SNAKE_CASE     :     PretrainedConfig
     _SCREAMING_SNAKE_CASE     :     str		="TensorFlow"



     @property
     def 			a__  (					self       ):
            return tf.__version__



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            # initialize GPU on separate process
            _A=	self.args.strategy
            if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
            _A=	self._prepare_inference_func(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            return self._measure_speed(_inference       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.args.strategy
            if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
            _A=	self._prepare_train_func(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            return self._measure_speed(_train       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            # initialize GPU on separate process
            if self.args.is_gpu:
                   tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,      lowerCAmelCase__       )
            _A=	self.args.strategy
            if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
            _A=	self._prepare_inference_func(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            return self._measure_memory(_inference       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if self.args.is_gpu:
                   tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,      lowerCAmelCase__       )
            _A=	self.args.strategy
            if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )

            _A=	self._prepare_train_func(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            return self._measure_memory(_train       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.config_dict[model_name]

            if self.args.fpaa:
                   raise NotImplementedError('Mixed precision is currently not supported.'       )

            _A=	(
                hasattr(lowerCAmelCase__				,      'architectures'       )
                and isinstance(config.architectures				,      lowerCAmelCase__       )
                and len(config.architectures       ) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                          _A=	'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                          _A=	__import__('transformers'				,      fromlist=[model_class]       )
                          _A=	getattr(lowerCAmelCase__				,      lowerCAmelCase__       )
                          _A=	model_cls(lowerCAmelCase__       )
                   except ImportError:
                          raise ImportError(
                              f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                              ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'       )
            else:
                   _A=	TF_MODEL_MAPPING[config.__class__](lowerCAmelCase__       )

            # encoder-decoder has vocab size saved differently
            _A=	config.vocab_size if hasattr(lowerCAmelCase__				,      'vocab_size'       ) else config.encoder.vocab_size
            _A=	random_input_ids(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

            @run_with_tf_optimizations(self.args.eager_mode				,      self.args.use_xla       )
            def encoder_decoder_forward():
                   return model(lowerCAmelCase__				,      decoder_input_ids=lowerCAmelCase__				,      training=lowerCAmelCase__       )

            @run_with_tf_optimizations(self.args.eager_mode				,      self.args.use_xla       )
            def encoder_forward():
                   return model(lowerCAmelCase__				,      training=lowerCAmelCase__       )

            _A=	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

            return _inference



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.config_dict[model_name]

            if self.args.eager_mode is not False:
                   raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'       )

            if self.args.fpaa:
                   raise NotImplementedError('Mixed precision is currently not supported.'       )

            _A=	(
                hasattr(lowerCAmelCase__				,      'architectures'       )
                and isinstance(config.architectures				,      lowerCAmelCase__       )
                and len(config.architectures       ) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                          _A=	'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                          _A=	__import__('transformers'				,      fromlist=[model_class]       )
                          _A=	getattr(lowerCAmelCase__				,      lowerCAmelCase__       )
                          _A=	model_cls(lowerCAmelCase__       )
                   except ImportError:
                          raise ImportError(
                              f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                              ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'       )
            else:
                   _A=	TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase__       )

            # encoder-decoder has vocab size saved differently
            _A=	config.vocab_size if hasattr(lowerCAmelCase__				,      'vocab_size'       ) else config.encoder.vocab_size
            _A=	random_input_ids(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

            @run_with_tf_optimizations(self.args.eager_mode				,      self.args.use_xla       )
            def encoder_decoder_train():
                   _A=	model(lowerCAmelCase__				,      decoder_input_ids=lowerCAmelCase__				,      labels=lowerCAmelCase__				,      training=lowerCAmelCase__       )[0]
                   _A=	tf.gradients(lowerCAmelCase__				,      model.trainable_variables       )
                   return gradients

            @run_with_tf_optimizations(self.args.eager_mode				,      self.args.use_xla       )
            def encoder_train():
                   _A=	model(lowerCAmelCase__				,      labels=lowerCAmelCase__				,      training=lowerCAmelCase__       )[0]
                   _A=	tf.gradients(lowerCAmelCase__				,      model.trainable_variables       )
                   return gradients

            _A=	encoder_decoder_train if config.is_encoder_decoder else encoder_train

            return _train



     def 			a__  (					self				,      lowerCAmelCase__       ):
            with self.args.strategy.scope():
                   try:
                          if self.args.is_tpu or self.args.use_xla:
                                 # run additional 10 times to stabilize compilation for tpu
                                 logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'       )
                                 timeit.repeat(lowerCAmelCase__				,      repeat=1				,      number=5       )

                          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                          _A=	timeit.repeat(
                              lowerCAmelCase__				,      repeat=self.args.repeat				,      number=10				,      )

                          return min(lowerCAmelCase__       ) / 10.0
                   except ResourceExhaustedError as e:
                          self.print_fn(f"Doesn't fit on GPU. {e}"       )





     def 			a__  (					self				,      lowerCAmelCase__       ):
            logger.info(
                'Note that TensorFlow allocates more memory than '
                'it might need to speed up computation. '
                'The memory reported here corresponds to the memory '
                'reported by `nvidia-smi`, which can vary depending '
                'on total available memory on the GPU that is used.'       )
            with self.args.strategy.scope():
                   try:
                          if self.args.trace_memory_line_by_line:
                                 if not self.args.eager_mode:
                                        raise ValueError(
                                            '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                                            ' consumption line by line.'       )
                                 _A=	start_memory_tracing('transformers'       )

                          if self.args.is_tpu:
                                 # tpu
                                 raise NotImplementedError(
                                     'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                                     ' with `args.memory=False`'       )
                          elif self.args.is_gpu:
                                 # gpu
                                 if not is_pyanvml_available():
                                        logger.warning(
                                            'py3nvml not installed, we won\'t log GPU memory usage. '
                                            'Install py3nvml (pip install py3nvml) to log information about GPU.'       )
                                        _A=	'N/A'
                                 else:
                                        logger.info(
                                            'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                                            ' running on the same GPU.'       )
                                        # init nvml
                                        nvml.nvmlInit()
                                        func()
                                        _A=	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
                                        _A=	nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase__       )
                                        _A=	meminfo.used
                                        _A=	Memory(lowerCAmelCase__       )
                                        # shutdown nvml
                                        nvml.nvmlShutdown()
                          else:
                                 # cpu
                                 if self.args.trace_memory_line_by_line:
                                        logger.info(
                                            'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                                            ' TensorFlow.'       )
                                        _A=	None
                                 else:
                                        _A=	measure_peak_memory_cpu(lowerCAmelCase__       )
                                        _A=	Memory(lowerCAmelCase__       ) if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) else memory_bytes
                          if self.args.trace_memory_line_by_line:
                                 _A=	stop_memory_tracing(lowerCAmelCase__       )
                                 if memory is None:
                                        _A=	summary.total
                          else:
                                 _A=	None

                          return memory, summary
                   except ResourceExhaustedError as e:
                          self.print_fn(f"Doesn't fit on GPU. {e}"       )
                          return "N/A", None
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       with open(lowerCAmelCase_       ,					'r'   ) as fh:
              fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_EX   )
              try:
                     print(*lowerCAmelCase_   )
              finally:
                     fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_UN   )


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							F"[{hostname}-{local_rank}]"

try:
      # test distributed
      dist.init_process_group('''nccl''')
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							dist.get_world_size()

      printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

      dist.barrier()
      if rank == 0:
            printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
      printflock(F"{gpu} is broken")
      raise