
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 sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase											=       '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCamelCase											=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCamelCase											=       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,							_KWARGS_DESCRIPTION     )
class A  (   datasets.Metric     ):






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             return datasets.MetricInfo(
                 description=_DESCRIPTION			,   citation=_CITATION			,   inputs_description=_KWARGS_DESCRIPTION			,   features=datasets.Features(self._get_feature_types() )			,   reference_urls=[
                     'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
                 ]			,   )






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             if self.config_name == "multilist":
                    return {
                        "predictions": datasets.Sequence(datasets.Value('float' ) ),
                        "references": datasets.Sequence(datasets.Value('float' ) ),
                    }
             else:
                    return {
                        "predictions": datasets.Value('float' ),
                        "references": datasets.Value('float' ),
                    }






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=None			,   lowercase_						:     Union[str, Any]="uniform_average"			,   lowercase_						:     Tuple=True )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=mean_squared_error(
                 lowercase_			,   lowercase_			,   sample_weight=lowercase_			,   multioutput=lowercase_			,   squared=lowercase_ )

             return {"mse": mse}


