

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

a		=logging.get_logger(__name__)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Tuple:
	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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[Any]:
	__lowerCamelCase				:				Union[str, Any]					       = random.Random()
	__lowerCamelCase				:				List[str]					       = [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        A_		(       __lowercase				):
							_UpperCAmelCase    :  TensorFlowBenchmarkArguments
							_UpperCAmelCase    :  PretrainedConfig
							_UpperCAmelCase    :  str						      =      "TensorFlow"
							@property
							def     lowerCAmelCase     (		self		:			int):
								return tf.__version__
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								# initialize GPU on separate process
								__lowerCamelCase				:				Union[str, Any]					       = self.args.strategy
								if strategy is None:
									raise ValueError('A device strategy has to be initialized before using TensorFlow.')
								__lowerCamelCase				:				Optional[int]					       = self._prepare_inference_func(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)
								return self._measure_speed(_inference)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				List[str]					       = self.args.strategy
								if strategy is None:
									raise ValueError('A device strategy has to be initialized before using TensorFlow.')
								__lowerCamelCase				:				List[Any]					       = self._prepare_train_func(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)
								return self._measure_speed(_train)
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								# initialize GPU on separate process
								if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,UpperCAmelCase__)
								__lowerCamelCase				:				Optional[int]					       = self.args.strategy
								if strategy is None:
									raise ValueError('A device strategy has to be initialized before using TensorFlow.')
								__lowerCamelCase				:				List[Any]					       = self._prepare_inference_func(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)
								return self._measure_memory(_inference)
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,UpperCAmelCase__)
								__lowerCamelCase				:				str					       = self.args.strategy
								if strategy is None:
									raise ValueError('A device strategy has to be initialized before using TensorFlow.')

								__lowerCamelCase				:				List[Any]					       = self._prepare_train_func(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)
								return self._measure_memory(_train)
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				str					       = self.config_dict[model_name]

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

								__lowerCamelCase				:				Tuple					       = (
								    hasattr(UpperCAmelCase__   ,'architectures')
								    and isinstance(config.architectures   ,UpperCAmelCase__)
								    and len(config.architectures) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
										__lowerCamelCase				:				Optional[int]					       = 'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
										__lowerCamelCase				:				str					       = __import__('transformers'   ,fromlist=[model_class])
										__lowerCamelCase				:				List[Any]					       = getattr(UpperCAmelCase__   ,UpperCAmelCase__)
										__lowerCamelCase				:				List[Any]					       = model_cls(UpperCAmelCase__)
									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:
									__lowerCamelCase				:				Dict					       = TF_MODEL_MAPPING[config.__class__](UpperCAmelCase__)

								# encoder-decoder has vocab size saved differently
								__lowerCamelCase				:				Any					       = config.vocab_size if hasattr(UpperCAmelCase__   ,'vocab_size') else config.encoder.vocab_size
								__lowerCamelCase				:				List[str]					       = random_input_ids(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)

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

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

								__lowerCamelCase				:				List[str]					       = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

								return _inference
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				Optional[int]					       = 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.')

								__lowerCamelCase				:				str					       = (
								    hasattr(UpperCAmelCase__   ,'architectures')
								    and isinstance(config.architectures   ,UpperCAmelCase__)
								    and len(config.architectures) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
										__lowerCamelCase				:				Any					       = 'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
										__lowerCamelCase				:				List[str]					       = __import__('transformers'   ,fromlist=[model_class])
										__lowerCamelCase				:				str					       = getattr(UpperCAmelCase__   ,UpperCAmelCase__)
										__lowerCamelCase				:				Optional[Any]					       = model_cls(UpperCAmelCase__)
									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:
									__lowerCamelCase				:				List[str]					       = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase__)

								# encoder-decoder has vocab size saved differently
								__lowerCamelCase				:				str					       = config.vocab_size if hasattr(UpperCAmelCase__   ,'vocab_size') else config.encoder.vocab_size
								__lowerCamelCase				:				Optional[Any]					       = random_input_ids(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__)

								@run_with_tf_optimizations(self.args.eager_mode   ,self.args.use_xla)
								def encoder_decoder_train():
									__lowerCamelCase				:				List[Any]					       = model(UpperCAmelCase__   ,decoder_input_ids=UpperCAmelCase__   ,labels=UpperCAmelCase__   ,training=UpperCAmelCase__)[0]
									__lowerCamelCase				:				List[str]					       = tf.gradients(UpperCAmelCase__   ,model.trainable_variables)
									return gradients

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

								__lowerCamelCase				:				Union[str, Any]					       = encoder_decoder_train if config.is_encoder_decoder else encoder_train

								return _train
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
								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(UpperCAmelCase__   ,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
										__lowerCamelCase				:				List[Any]					       = timeit.repeat(
										    UpperCAmelCase__   ,repeat=self.args.repeat   ,number=1_0   ,)

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


							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Callable[[], None]):
								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.')
											__lowerCamelCase				:				Tuple					       = 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.')
												__lowerCamelCase				:				Union[str, Any]					       = '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()
												__lowerCamelCase				:				Tuple					       = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
												__lowerCamelCase				:				Tuple					       = nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase__)
												__lowerCamelCase				:				Optional[int]					       = meminfo.used
												__lowerCamelCase				:				Tuple					       = Memory(UpperCAmelCase__)
												# 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.')
												__lowerCamelCase				:				List[Any]					       = None
											else:
												__lowerCamelCase				:				Optional[Any]					       = measure_peak_memory_cpu(UpperCAmelCase__)
												__lowerCamelCase				:				Optional[Any]					       = Memory(UpperCAmelCase__) if isinstance(UpperCAmelCase__   ,UpperCAmelCase__) else memory_bytes
										if self.args.trace_memory_line_by_line:
											__lowerCamelCase				:				Tuple					       = stop_memory_tracing(UpperCAmelCase__)
											if memory is None:
												__lowerCamelCase				:				Any					       = summary.total
										else:
											__lowerCamelCase				:				Union[str, Any]					       = None

										return memory, summary
									except ResourceExhaustedError as e:
										self.print_fn(F"Doesn\'t fit on GPU. {e}")
										return "N/A", None








'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu

@require_cpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[Any]:
        lowerCAmelCase     =			torch.nn.Linear(1_0    ,      1_0    )
        lowerCAmelCase     =			torch.optim.SGD(model.parameters()    ,      0.1    )
        lowerCAmelCase     =			Accelerator()
        lowerCAmelCase     =			accelerator.prepare(UpperCAmelCase__    )
        try:
           pickle.loads(pickle.dumps(UpperCAmelCase__    )    )
        except Exception as e:
           self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
        AcceleratorState._reset_state()





