


'''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

__snake_case    						=logging.get_logger(__name__)





def 	a_					( lowerCamelCase	:     bool	, lowerCamelCase	:     bool				):
   def run_func(lowerCamelCase	:     List[str]				):
      @wraps(lowerCamelCase				)
      def run_in_eager_mode(*lowerCamelCase	:     List[str]	, **lowerCamelCase	:     Tuple				):
         return func(*lowerCamelCase	, **lowerCamelCase				)

      @wraps(lowerCamelCase				)
      @tf.function(experimental_compile=lowerCamelCase				)
      def run_in_graph_mode(*lowerCamelCase	:     List[Any]	, **lowerCamelCase	:     Any				):
         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	:     int	, lowerCamelCase	:     int	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			random.Random()
   lowerCAmelCase     =			[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_      (  __lowercase		):
     lowerCamelCase				:	TensorFlowBenchmarkArguments
     lowerCamelCase				:	PretrainedConfig
     lowerCamelCase				:	str		=     "TensorFlow"





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        return tf.__version__





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  float:
        # initialize GPU on separate process
        lowerCAmelCase     =			self.args.strategy
        if strategy is None:
           raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )
        lowerCAmelCase     =			self._prepare_inference_func(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        return self._measure_speed(_inference    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  float:
        lowerCAmelCase     =			self.args.strategy
        if strategy is None:
           raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )
        lowerCAmelCase     =			self._prepare_train_func(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        return self._measure_speed(_train    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  [Memory, Optional[MemorySummary]]:
        # 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     =			self.args.strategy
        if strategy is None:
           raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )
        lowerCAmelCase     =			self._prepare_inference_func(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        return self._measure_memory(_inference    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  [Memory, Optional[MemorySummary]]:
        if self.args.is_gpu:
           tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			self.args.strategy
        if strategy is None:
           raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )

        lowerCAmelCase     =			self._prepare_train_func(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        return self._measure_memory(_train    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  Callable[[], None]:
        lowerCAmelCase     =			self.config_dict[model_name]

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

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

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase     =			config.vocab_size if hasattr(UpperCAmelCase__    ,      'vocab_size'    ) else config.encoder.vocab_size
        lowerCAmelCase     =			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     =			encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

        return _inference





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  Callable[[], None]:
        lowerCAmelCase     =			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     =			(
            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     =			'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
              lowerCAmelCase     =			__import__('transformers'    ,      fromlist=[model_class]    )
              lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )
              lowerCAmelCase     =			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     =			TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase__    )

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase     =			config.vocab_size if hasattr(UpperCAmelCase__    ,      'vocab_size'    ) else config.encoder.vocab_size
        lowerCAmelCase     =			random_input_ids(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

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

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

        lowerCAmelCase     =			encoder_decoder_train if config.is_encoder_decoder else encoder_train

        return _train





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]    )				->  float:
        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     =			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 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Callable[[], None]    )				->  [Memory, MemorySummary]:
        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     =			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     =			'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     =			nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
                    lowerCAmelCase     =			nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase__    )
                    lowerCAmelCase     =			meminfo.used
                    lowerCAmelCase     =			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     =			None
                 else:
                    lowerCAmelCase     =			measure_peak_memory_cpu(UpperCAmelCase__    )
                    lowerCAmelCase     =			Memory(UpperCAmelCase__    ) if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else memory_bytes
              if self.args.trace_memory_line_by_line:
                 lowerCAmelCase     =			stop_memory_tracing(UpperCAmelCase__    )
                 if memory is None:
                    lowerCAmelCase     =			summary.total
              else:
                 lowerCAmelCase     =			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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""roberta-prelayernorm"""



      def __init__(						self    ,			lowercase=5_02_65    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=1    ,			lowercase=0    ,			lowercase=2    ,			lowercase="absolute"    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase    ,			bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)

        a__:     Union[str, Any]										=	vocab_size
        a__:     str										=	hidden_size
        a__:     Tuple										=	num_hidden_layers
        a__:     List[str]										=	num_attention_heads
        a__:     Dict										=	hidden_act
        a__:     int										=	intermediate_size
        a__:     Tuple										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     Tuple										=	max_position_embeddings
        a__:     Tuple										=	type_vocab_size
        a__:     Optional[Any]										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Optional[int]										=	position_embedding_type
        a__:     Any										=	use_cache
        a__:     Dict										=	classifier_dropout







class    __snake_case       (     __lowerCAmelCase				):



      @property
      def   lowerCamelCase_   (						self)	->			Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        if self.task == "multiple-choice":
          a__:     str										=	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          a__:     Union[str, Any]										=	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ])


