
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

lowerCamelCase_						: List[str]         =						logging.get_logger(__name__)







def        A__					(						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        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							Optional[int]	=						random.Random()
				UpperCamelCase_:							Tuple	=						[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        _UpperCamelCase				(					__A  ):






			'''simple docstring'''

			__UpperCamelCase      :						TensorFlowBenchmarkArguments
			__UpperCamelCase      :						PretrainedConfig
			__UpperCamelCase      :						str					      = "TensorFlow"

			@property
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							return tf.__version__

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Any						,	snake_case_ :       Dict						,	snake_case_ :       Union[str, Any]      ):
							# initialize GPU on separate process
							UpperCamelCase_:							List[str]	=						self.args.strategy
							if strategy is None:
											raise ValueError("""A device strategy has to be initialized before using TensorFlow."""      )
							UpperCamelCase_:							Tuple	=						self._prepare_inference_func(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )
							return self._measure_speed(_inference      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       str						,	snake_case_ :       Any      ):
							UpperCamelCase_:							List[Any]	=						self.args.strategy
							if strategy is None:
											raise ValueError("""A device strategy has to be initialized before using TensorFlow."""      )
							UpperCamelCase_:							Union[str, Any]	=						self._prepare_train_func(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )
							return self._measure_speed(_train      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Tuple      ):
							# 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__      )
							UpperCamelCase_:							Optional[int]	=						self.args.strategy
							if strategy is None:
											raise ValueError("""A device strategy has to be initialized before using TensorFlow."""      )
							UpperCamelCase_:							Tuple	=						self._prepare_inference_func(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )
							return self._measure_memory(_inference      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Any						,	snake_case_ :       Dict						,	snake_case_ :       int      ):
							if self.args.is_gpu:
											tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,	UpperCamelCase__      )
							UpperCamelCase_:							List[str]	=						self.args.strategy
							if strategy is None:
											raise ValueError("""A device strategy has to be initialized before using TensorFlow."""      )

							UpperCamelCase_:							List[Any]	=						self._prepare_train_func(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )
							return self._measure_memory(_train      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Any						,	snake_case_ :       Tuple      ):
							UpperCamelCase_:							Union[str, Any]	=						self.config_dict[model_name]

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

							UpperCamelCase_:							Optional[int]	=						(
							    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:
															UpperCamelCase_:							List[str]	=						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
															UpperCamelCase_:							Tuple	=						__import__("""transformers"""						,	fromlist=[model_class]      )
															UpperCamelCase_:							List[str]	=						getattr(UpperCamelCase__						,	UpperCamelCase__      )
															UpperCamelCase_:							Tuple	=						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:
											UpperCamelCase_:							int	=						TF_MODEL_MAPPING[config.__class__](UpperCamelCase__      )

							# encoder-decoder has vocab size saved differently
							UpperCamelCase_:							Any	=						config.vocab_size if hasattr(UpperCamelCase__						,	"""vocab_size"""      ) else config.encoder.vocab_size
							UpperCamelCase_:							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__      )

							UpperCamelCase_:							Union[str, Any]	=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       str						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							List[str]	=						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."""      )

							UpperCamelCase_:							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:
															UpperCamelCase_:							Dict	=						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
															UpperCamelCase_:							str	=						__import__("""transformers"""						,	fromlist=[model_class]      )
															UpperCamelCase_:							Union[str, Any]	=						getattr(UpperCamelCase__						,	UpperCamelCase__      )
															UpperCamelCase_:							Dict	=						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:
											UpperCamelCase_:							Optional[int]	=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__      )

							# encoder-decoder has vocab size saved differently
							UpperCamelCase_:							int	=						config.vocab_size if hasattr(UpperCamelCase__						,	"""vocab_size"""      ) else config.encoder.vocab_size
							UpperCamelCase_:							List[Any]	=						random_input_ids(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )

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

							@run_with_tf_optimizations(self.args.eager_mode						,	self.args.use_xla      )
							def encoder_train():
											UpperCamelCase_:							Union[str, Any]	=						model(UpperCamelCase__						,	labels=UpperCamelCase__						,	training=UpperCamelCase__      )[0]
											UpperCamelCase_:							Optional[int]	=						tf.gradients(UpperCamelCase__						,	model.trainable_variables      )
											return gradients

							UpperCamelCase_:							Tuple	=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Tuple      ):
							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
															UpperCamelCase_:							Optional[int]	=						timeit.repeat(
															    UpperCamelCase__						,	repeat=self.args.repeat						,	number=10						,	)

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




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Tuple      ):
							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."""      )
																			UpperCamelCase_:							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."""      )
																							UpperCamelCase_:							int	=						'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()
																							UpperCamelCase_:							Tuple	=						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																							UpperCamelCase_:							int	=						nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__      )
																							UpperCamelCase_:							Optional[int]	=						meminfo.used
																							UpperCamelCase_:							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."""      )
																							UpperCamelCase_:							Any	=						None
																			else:
																							UpperCamelCase_:							Any	=						measure_peak_memory_cpu(UpperCamelCase__      )
																							UpperCamelCase_:							Any	=						Memory(UpperCamelCase__      ) if isinstance(UpperCamelCase__						,	UpperCamelCase__      ) else memory_bytes
															if self.args.trace_memory_line_by_line:
																			UpperCamelCase_:							Optional[int]	=						stop_memory_tracing(UpperCamelCase__      )
																			if memory is None:
																							UpperCamelCase_:							str	=						summary.total
															else:
																			UpperCamelCase_:							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








def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				while second != 0:
								UpperCamelCase_:							Optional[Any]	=						first & second
								first ^= second
								UpperCamelCase_:							Any	=						c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCamelCase_						: List[Any]         =  int(input("""Enter the first number: """).strip())
							lowerCamelCase_						: Tuple         =  int(input("""Enter the second number: """).strip())
							print(F"""{add(first, second) = }""")






