



"""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

_lowerCAmelCase    :Any     =					logging.get_logger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		bool     ,       UpperCamelCase__		:		bool							):
    def run_func(UpperCamelCase__		:		List[Any]							):
        @wraps(UpperCamelCase__							)
        def run_in_eager_mode(*UpperCamelCase__		:		List[str]     ,       **UpperCamelCase__		:		Any							):
            return func(*UpperCamelCase__     ,       **UpperCamelCase__							)

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

        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_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Tuple											=    random.Random()
    _UpperCAmelCase							:  int											=    [rng.randint(0     ,       vocab_size - 1							) for i in range(batch_size * sequence_length							)]
    return tf.constant(UpperCamelCase__     ,       shape=(batch_size, sequence_length)     ,       dtype=tf.intaa							)


class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=42
     a__									=42
     a__									="TensorFlow"





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return tf.__version__





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





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





     def  __lowerCAmelCase	(					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		)
         _UpperCAmelCase							:  List[str]											=    self.args.strategy
         if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''		)
         _UpperCAmelCase							:  Tuple											=    self._prepare_inference_func(A       ,	A       ,	A		)
         return self._measure_memory(_inference		)





     def  __lowerCAmelCase	(					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		)
         _UpperCAmelCase							:  Dict											=    self.args.strategy
         if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''		)

         _UpperCAmelCase							:  Optional[int]											=    self._prepare_train_func(A       ,	A       ,	A		)
         return self._measure_memory(_train		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					Callable[[], None]:
         _UpperCAmelCase							:  Dict											=    self.config_dict[model_name]

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

         _UpperCAmelCase							:  Dict											=    (
             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:
                 _UpperCAmelCase							:  Tuple											=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                 _UpperCAmelCase							:  Optional[Any]											=    __import__('''transformers'''       ,	fromlist=[model_class]		)
                 _UpperCAmelCase							:  Any											=    getattr(A       ,	A		)
                 _UpperCAmelCase							:  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:
             _UpperCAmelCase							:  str											=    TF_MODEL_MAPPING[config.__class__](A		)

         # encoder-decoder has vocab size saved differently
         _UpperCAmelCase							:  Optional[Any]											=    config.vocab_size if hasattr(A       ,	'''vocab_size'''		) else config.encoder.vocab_size
         _UpperCAmelCase							:  List[Any]											=    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		)

         _UpperCAmelCase							:  Dict											=    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

         return _inference





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					Callable[[], None]:
         _UpperCAmelCase							:  Union[str, Any]											=    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.'''		)

         _UpperCAmelCase							:  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:
                 _UpperCAmelCase							:  Optional[int]											=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                 _UpperCAmelCase							:  Any											=    __import__('''transformers'''       ,	fromlist=[model_class]		)
                 _UpperCAmelCase							:  List[str]											=    getattr(A       ,	A		)
                 _UpperCAmelCase							:  Union[str, 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:
             _UpperCAmelCase							:  Union[str, Any]											=    TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A		)

         # encoder-decoder has vocab size saved differently
         _UpperCAmelCase							:  str											=    config.vocab_size if hasattr(A       ,	'''vocab_size'''		) else config.encoder.vocab_size
         _UpperCAmelCase							:  int											=    random_input_ids(A       ,	A       ,	A		)

         @run_with_tf_optimizations(self.args.eager_mode       ,	self.args.use_xla		)
         def encoder_decoder_train():
             _UpperCAmelCase							:  Tuple											=    model(A       ,	decoder_input_ids=A       ,	labels=A       ,	training=A		)[0]
             _UpperCAmelCase							:  Union[str, Any]											=    tf.gradients(A       ,	model.trainable_variables		)
             return gradients

         @run_with_tf_optimizations(self.args.eager_mode       ,	self.args.use_xla		)
         def encoder_train():
             _UpperCAmelCase							:  Dict											=    model(A       ,	labels=A       ,	training=A		)[0]
             _UpperCAmelCase							:  List[Any]											=    tf.gradients(A       ,	model.trainable_variables		)
             return gradients

         _UpperCAmelCase							:  Any											=    encoder_decoder_train if config.is_encoder_decoder else encoder_train

         return _train





     def  __lowerCAmelCase	(					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
                 _UpperCAmelCase							:  List[str]											=    timeit.repeat(
                     A       ,	repeat=self.args.repeat       ,	number=1_0       ,	)

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





     def  __lowerCAmelCase	(					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.'''		)
                     _UpperCAmelCase							:  Optional[Any]											=    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.'''		)
                         _UpperCAmelCase							:  Optional[Any]											=    '''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()
                         _UpperCAmelCase							:  int											=    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
                         _UpperCAmelCase							:  int											=    nvml.nvmlDeviceGetMemoryInfo(A		)
                         _UpperCAmelCase							:  List[Any]											=    meminfo.used
                         _UpperCAmelCase							:  Any											=    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.'''		)
                         _UpperCAmelCase							:  Union[str, Any]											=    None
                     else:
                         _UpperCAmelCase							:  Optional[int]											=    measure_peak_memory_cpu(A		)
                         _UpperCAmelCase							:  int											=    Memory(A		) if isinstance(A       ,	A		) else memory_bytes
                 if self.args.trace_memory_line_by_line:
                     _UpperCAmelCase							:  List[Any]											=    stop_memory_tracing(A		)
                     if memory is None:
                         _UpperCAmelCase							:  Tuple											=    summary.total
                 else:
                     _UpperCAmelCase							:  int											=    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


