
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			:     int           =   logging.get_logger(__name__)

def a__    (       A_,     A_					):






  '''simple docstring'''





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

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

    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__    (       A_,     A_,     A_					):






  '''simple docstring'''





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





class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		42
  a__           =		42
  a__           =		"TensorFlow"
  @property
  def 							_lowercase							( self			: Tuple    )			->	Optional[Any]:







    """simple docstring"""






    return tf.__version__
  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	float:







    """simple docstring"""






    __magic_name__				     =       self.args.strategy
    if strategy is None:
      raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
    __magic_name__				     =       self._prepare_inference_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return self._measure_speed(_inference    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	float:







    """simple docstring"""






    __magic_name__				     =       self.args.strategy
    if strategy is None:
      raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
    __magic_name__				     =       self._prepare_train_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return self._measure_speed(_train    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	[Memory, Optional[MemorySummary]]:







    """simple docstring"""






    if self.args.is_gpu:
      tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,							UpperCamelCase__    )
    __magic_name__				     =       self.args.strategy
    if strategy is None:
      raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
    __magic_name__				     =       self._prepare_inference_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return self._measure_memory(_inference    )
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	[Memory, Optional[MemorySummary]]:







    """simple docstring"""






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

    __magic_name__				     =       self._prepare_train_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return self._measure_memory(_train    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	Callable[[], None]:







    """simple docstring"""






    __magic_name__				     =       self.config_dict[model_name]

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

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

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

    __magic_name__				     =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

    return _inference
  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	Callable[[], None]:







    """simple docstring"""






    __magic_name__				     =       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."""    )

    __magic_name__				     =       (
        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:
        __magic_name__				     =       """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
        __magic_name__				     =       __import__("""transformers"""			,							fromlist=[model_class]    )
        __magic_name__				     =       getattr(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       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:
      __magic_name__				     =       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__    )

    # encoder-decoder has vocab size saved differently
    __magic_name__				     =       config.vocab_size if hasattr(UpperCamelCase__			,							"""vocab_size"""    ) else config.encoder.vocab_size
    __magic_name__				     =       random_input_ids(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )

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

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

    __magic_name__				     =       encoder_decoder_train if config.is_encoder_decoder else encoder_train

    return _train
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Optional[int]    )			->	float:







    """simple docstring"""






    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
        __magic_name__				     =       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 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: Callable[[], None]    )			->	[Memory, MemorySummary]:







    """simple docstring"""






    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."""    )
          __magic_name__				     =       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."""    )
            __magic_name__				     =       """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()
            __magic_name__				     =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
            __magic_name__				     =       nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__    )
            __magic_name__				     =       meminfo.used
            __magic_name__				     =       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."""    )
            __magic_name__				     =       None
          else:
            __magic_name__				     =       measure_peak_memory_cpu(UpperCamelCase__    )
            __magic_name__				     =       Memory(UpperCamelCase__    ) if isinstance(UpperCamelCase__			,							UpperCamelCase__    ) else memory_bytes
        if self.args.trace_memory_line_by_line:
          __magic_name__				     =       stop_memory_tracing(UpperCamelCase__    )
          if memory is None:
            __magic_name__				     =       summary.total
        else:
          __magic_name__				     =       None

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









from __future__ import annotations

from math import pi






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float		):

  if (inductance, frequency, reactance).count(0		) != 1:
    raise ValueError("""One and only one argument must be 0"""		)
  if inductance < 0:
    raise ValueError("""Inductance cannot be negative"""		)
  if frequency < 0:
    raise ValueError("""Frequency cannot be negative"""		)
  if reactance < 0:
    raise ValueError("""Inductive reactance cannot be negative"""		)
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


