




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

            @wraps(snake_case_     )
            @tf.function(experimental_compile=snake_case_     )
            def run_in_graph_mode(*snake_case_,**snake_case_     ):
                  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 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						random.Random()
      _A							:     Optional[int]			=						[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							(   UpperCamelCase__		):
     _a							 =						42
     _a							 =						42
     _a							 =						"TensorFlow"

     @property
     def 					a__	(  self				)						->							Any:
           return tf.__version__

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							float:
           # initialize GPU on separate process
           _A							:     List[Any]			=						self.args.strategy
           if strategy is None:
                 raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
           _A							:     Optional[Any]			=						self._prepare_inference_func(_a		,     _a		,     _a				)
           return self._measure_speed(_inference				)

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							float:
           _A							:     Optional[int]			=						self.args.strategy
           if strategy is None:
                 raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
           _A							:     Dict			=						self._prepare_train_func(_a		,     _a		,     _a				)
           return self._measure_speed(_train				)

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							[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]		,     _a				)
           _A							:     Dict			=						self.args.strategy
           if strategy is None:
                 raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
           _A							:     Dict			=						self._prepare_inference_func(_a		,     _a		,     _a				)
           return self._measure_memory(_inference				)

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

           _A							:     str			=						self._prepare_train_func(_a		,     _a		,     _a				)
           return self._measure_memory(_train				)

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							Callable[[], None]:
           _A							:     Optional[Any]			=						self.config_dict[model_name]

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

           _A							:     List[Any]			=						(
               hasattr(_a		,     """architectures"""				)
               and isinstance(config.architectures		,     _a				)
               and len(config.architectures				) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _A							:     Dict			=						"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
                       _A							:     Tuple			=						__import__("""transformers"""		,     fromlist=[model_class]				)
                       _A							:     Tuple			=						getattr(_a		,     _a				)
                       _A							:     Optional[int]			=						model_cls(_a				)
                 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							:     Union[str, Any]			=						TF_MODEL_MAPPING[config.__class__](_a				)

           # encoder-decoder has vocab size saved differently
           _A							:     Any			=						config.vocab_size if hasattr(_a		,     """vocab_size"""				) else config.encoder.vocab_size
           _A							:     Dict			=						random_input_ids(_a		,     _a		,     _a				)

           @run_with_tf_optimizations(self.args.eager_mode		,     self.args.use_xla				)
           def encoder_decoder_forward():
                 return model(_a		,     decoder_input_ids=_a		,     training=_a				)

           @run_with_tf_optimizations(self.args.eager_mode		,     self.args.use_xla				)
           def encoder_forward():
                 return model(_a		,     training=_a				)

           _A							:     str			=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

           return _inference

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							Callable[[], None]:
           _A							:     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."""				)

           _A							:     str			=						(
               hasattr(_a		,     """architectures"""				)
               and isinstance(config.architectures		,     _a				)
               and len(config.architectures				) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _A							:     List[str]			=						"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
                       _A							:     Union[str, Any]			=						__import__("""transformers"""		,     fromlist=[model_class]				)
                       _A							:     Any			=						getattr(_a		,     _a				)
                       _A							:     List[Any]			=						model_cls(_a				)
                 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							:     Optional[Any]			=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_a				)

           # encoder-decoder has vocab size saved differently
           _A							:     str			=						config.vocab_size if hasattr(_a		,     """vocab_size"""				) else config.encoder.vocab_size
           _A							:     Optional[int]			=						random_input_ids(_a		,     _a		,     _a				)

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

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

           _A							:     List[str]			=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

           return _train

     def 					a__	(  self		,     _a				)						->							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(_a		,     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							:     Union[str, Any]			=						timeit.repeat(
                           _a		,     repeat=self.args.repeat		,     number=10		,     )

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



     def 					a__	(  self		,     _a				)						->							[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."""				)
                             _A							:     int			=						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							:     List[str]			=						"""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							:     str			=						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
                                   _A							:     Optional[int]			=						nvml.nvmlDeviceGetMemoryInfo(_a				)
                                   _A							:     int			=						meminfo.used
                                   _A							:     Tuple			=						Memory(_a				)
                                   # 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							:     Optional[Any]			=						None
                             else:
                                   _A							:     Tuple			=						measure_peak_memory_cpu(_a				)
                                   _A							:     int			=						Memory(_a				) if isinstance(_a		,     _a				) else memory_bytes
                       if self.args.trace_memory_line_by_line:
                             _A							:     Optional[int]			=						stop_memory_tracing(_a				)
                             if memory is None:
                                   _A							:     Tuple			=						summary.total
                       else:
                             _A							:     Optional[Any]			=						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),
            ])


