


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

          @wraps(SCREAMING_SNAKE_CASE__				)
          @tf.function(experimental_compile=SCREAMING_SNAKE_CASE__				)
          def run_in_graph_mode(*SCREAMING_SNAKE_CASE__			:					Dict			,     **SCREAMING_SNAKE_CASE__			:					List[str]				):
               return func(*SCREAMING_SNAKE_CASE__			,     **SCREAMING_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				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=random.Random()
     __UpperCamelCase    							=[rng.randint(0			,     vocab_size - 1				) for i in range(batch_size * sequence_length				)]
     return tf.constant(SCREAMING_SNAKE_CASE__			,     shape=(batch_size, sequence_length)			,     dtype=tf.intaa				)






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		TensorFlowBenchmarkArguments
    UpperCAmelCase__						:		PretrainedConfig
    UpperCAmelCase__						:		str      =  "TensorFlow"






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






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






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       float:
         __UpperCamelCase    							=self.args.strategy
         if strategy is None:
              raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )
         __UpperCamelCase    							=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_    )
         __UpperCamelCase    							=self.args.strategy
         if strategy is None:
              raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )
         __UpperCamelCase    							=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_    )
         __UpperCamelCase    							=self.args.strategy
         if strategy is None:
              raise ValueError('A device strategy has to be initialized before using TensorFlow.'    )

         __UpperCamelCase    							=self._prepare_train_func(A_					,   A_					,   A_    )
         return self._measure_memory(_train    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Callable[[], None]:
         __UpperCamelCase    							=self.config_dict[model_name]

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

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

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

         return _inference






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Callable[[], None]:
         __UpperCamelCase    							=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    							=(
             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    							='TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   __UpperCamelCase    							=__import__('transformers'					,   fromlist=[model_class]    )
                   __UpperCamelCase    							=getattr(A_					,   A_    )
                   __UpperCamelCase    							=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    							=TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A_    )

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

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

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

         __UpperCamelCase    							=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
                   __UpperCamelCase    							=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.'    )
                        __UpperCamelCase    							=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    							='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    							=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
                             __UpperCamelCase    							=nvml.nvmlDeviceGetMemoryInfo(A_    )
                             __UpperCamelCase    							=meminfo.used
                             __UpperCamelCase    							=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    							=None
                        else:
                             __UpperCamelCase    							=measure_peak_memory_cpu(A_    )
                             __UpperCamelCase    							=Memory(A_    ) if isinstance(A_					,   A_    ) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                        __UpperCamelCase    							=stop_memory_tracing(A_    )
                        if memory is None:
                             __UpperCamelCase    							=summary.total
                   else:
                        __UpperCamelCase    							=None

                   return memory, summary
              except ResourceExhaustedError as e:
                   self.print_fn(f'Doesn\'t fit on GPU. {e}'    )
                   return "N/A", None









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





