


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 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->List[str]:







      """simple docstring"""

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

            @wraps(snake_case_   )
            @tf.function(experimental_compile=snake_case_   )
            def run_in_graph_mode(*_UpperCamelCase,			**_UpperCamelCase   ):
                  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 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->List[str]:







      """simple docstring"""

      lowercase	:      List[Any]          =						random.Random()
      lowercase	:      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  __SCREAMING_SNAKE_CASE      (	UpperCamelCase__      ):
      A :							Optional[int]										=   42
      A :							Tuple										=   42
      A :							int										=   'TensorFlow'


      @property
      def 				__lowerCamelCase	(							self						):
            return tf.__version__


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


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      Optional[int]          =						self.args.strategy
            if strategy is None:
                  raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
            lowercase	:      Dict          =						self._prepare_train_func(_a							,  _a							,  _a						)
            return self._measure_speed(_train						)


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            # 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						)
            lowercase	:      Dict          =						self.args.strategy
            if strategy is None:
                  raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
            lowercase	:      Dict          =						self._prepare_inference_func(_a							,  _a							,  _a						)
            return self._measure_memory(_inference						)


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            if self.args.is_gpu:
                  tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,  _a						)
            lowercase	:      str          =						self.args.strategy
            if strategy is None:
                  raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)

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


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      Optional[Any]          =						self.config_dict[model_name]

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

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

            # encoder-decoder has vocab size saved differently
            lowercase	:      Any          =						config.vocab_size if hasattr(_a							,  '''vocab_size'''						) else config.encoder.vocab_size
            lowercase	:      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						)

            lowercase	:      str          =						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

            return _inference


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      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.'''						)

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

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

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

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

            return _train


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            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
                        lowercase	:      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 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            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.'''						)
                              lowercase	:      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.'''						)
                                    lowercase	:      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()
                                    lowercase	:      str          =						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx						)
                                    lowercase	:      Optional[int]          =						nvml.nvmlDeviceGetMemoryInfo(_a						)
                                    lowercase	:      int          =						meminfo.used
                                    lowercase	:      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.'''						)
                                    lowercase	:      Optional[Any]          =						None
                              else:
                                    lowercase	:      Tuple          =						measure_peak_memory_cpu(_a						)
                                    lowercase	:      int          =						Memory(_a						) if isinstance(_a							,  _a						) else memory_bytes
                        if self.args.trace_memory_line_by_line:
                              lowercase	:      Optional[int]          =						stop_memory_tracing(_a						)
                              if memory is None:
                                    lowercase	:      Tuple          =						summary.total
                        else:
                              lowercase	:      Optional[Any]          =						None

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







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(snake_case_     )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      _A							:     Any			=						36

      _A							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,snake_case_     )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()

