




"""simple docstring"""

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 						UpperCAmelCase__					( _snake_case							: bool    ,  _snake_case							: bool							)   -> Optional[int]:






      '''simple docstring'''


      def run_func(_snake_case							: List[str]							):
            @wraps(_snake_case							)
            def run_in_eager_mode(*_snake_case							: Optional[Any]    ,  **_snake_case							: List[str]							):
                  return func(*_snake_case    ,  **_snake_case							)

            @wraps(_snake_case							)
            @tf.function(experimental_compile=_snake_case							)
            def run_in_graph_mode(*_snake_case							: Optional[Any]    ,  **_snake_case							: str							):
                  return func(*_snake_case    ,  **_snake_case							)

            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__					( _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: int							)   -> ["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(_snake_case    ,  shape=(batch_size, sequence_length)    ,  dtype=tf.intaa							)







class     lowercase_	(					_a							):



 '''simple docstring'''
 UpperCAmelCase   :					TensorFlowBenchmarkArguments
 UpperCAmelCase   :					PretrainedConfig
 UpperCAmelCase   :					str				  =     "TensorFlow"

 @property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       return tf.__version__

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # 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       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _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       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	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]   ,							__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       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       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       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _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       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _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       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	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(__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       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	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.'		)
                         _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







"""simple docstring"""

from sklearn.metrics import fa_score

import datasets


a         =      '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


a         =      '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


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





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



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Any		):
       return datasets.MetricInfo(
           description=_DESCRIPTION   ,							citation=_CITATION   ,							inputs_description=_KWARGS_DESCRIPTION   ,							features=datasets.Features(
               {
                   'predictions': datasets.Sequence(datasets.Value('int32'		)		),
                   'references': datasets.Sequence(datasets.Value('int32'		)		),
               }
               if self.config_name == 'multilabel'
               else {
                   'predictions': datasets.Value('int32'		),
                   'references': datasets.Value('int32'		),
               }		)   ,							reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']   ,							)



 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	List[str]=None   ,							_UpperCAmelCase	:	Dict=1   ,							_UpperCAmelCase	:	Union[str, Any]="binary"   ,							_UpperCAmelCase	:	List[str]=None		):
       _A        =       fa_score(
           _UpperCAmelCase   ,							_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							pos_label=_UpperCAmelCase   ,							average=_UpperCAmelCase   ,							sample_weight=_UpperCAmelCase		)
       return {"f1": float(_UpperCAmelCase		) if score.size == 1 else score}

