






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	:		str										=   logging.get_logger(__name__)







def 			A__							(      _a  :					bool		,  _a  :					bool	):





 '''simple docstring'''







 def run_func(_a  :					int	):
  @wraps(_a	)
  def run_in_eager_mode(*_a  :					List[str]		,  **_a  :					Optional[Any]	):
   return func(*_a		,  **_a	)

  @wraps(_a	)
  @tf.function(experimental_compile=_a	)
  def run_in_graph_mode(*_a  :					int		,  **_a  :					Optional[int]	):
   return func(*_a		,  **_a	)

  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__							(      _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =random.Random()
 snake_case__  :    List[Any]							       =[rng.randint(0		,  vocab_size - 1	) for i in range(batch_size * sequence_length	)]
 return tf.constant(_a		,  shape=(batch_size, sequence_length)		,  dtype=tf.intaa	)







class  _lowercase   (   _A						):
      _a							:						TensorFlowBenchmarkArguments
      _a							:						PretrainedConfig
      _a							:						str										=							"TensorFlow"
      @property
      def 						lowercase__					(     self   ):
       return tf.__version__
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       # initialize GPU on separate process
       snake_case__  :    Optional[Any]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Union[str, Any]							       =self._prepare_inference_func(a				,			a				,			a   )
       return self._measure_speed(_inference   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Union[str, Any]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Optional[int]							       =self._prepare_train_func(a				,			a				,			a   )
       return self._measure_speed(_train   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       # 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   )
       snake_case__  :    Tuple							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )
       snake_case__  :    Union[str, Any]							       =self._prepare_inference_func(a				,			a				,			a   )
       return self._measure_memory(_inference   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,			a   )
       snake_case__  :    List[str]							       =self.args.strategy
       if strategy is None:
        raise ValueError("""A device strategy has to be initialized before using TensorFlow."""   )

       snake_case__  :    Any							       =self._prepare_train_func(a				,			a				,			a   )
       return self._measure_memory(_train   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =self.config_dict[model_name]

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

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

       # encoder-decoder has vocab size saved differently
       snake_case__  :    Optional[Any]							       =config.vocab_size if hasattr(a				,			"""vocab_size"""   ) else config.encoder.vocab_size
       snake_case__  :    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   )

       snake_case__  :    Tuple							       =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =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."""   )

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

       # encoder-decoder has vocab size saved differently
       snake_case__  :    int							       =config.vocab_size if hasattr(a				,			"""vocab_size"""   ) else config.encoder.vocab_size
       snake_case__  :    Union[str, Any]							       =random_input_ids(a				,			a				,			a   )

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

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

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

       return _train
      def 						lowercase__					(     self				,			a   ):
       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
         snake_case__  :    Dict							       =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 						lowercase__					(     self				,			a   ):
       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."""   )
          snake_case__  :    Tuple							       =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."""   )
           snake_case__  :    Tuple							       ="""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()
           snake_case__  :    Dict							       =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
           snake_case__  :    Tuple							       =nvml.nvmlDeviceGetMemoryInfo(a   )
           snake_case__  :    Dict							       =meminfo.used
           snake_case__  :    List[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."""   )
           snake_case__  :    Dict							       =None
          else:
           snake_case__  :    Union[str, Any]							       =measure_peak_memory_cpu(a   )
           snake_case__  :    int							       =Memory(a   ) if isinstance(a				,			a   ) else memory_bytes
         if self.args.trace_memory_line_by_line:
          snake_case__  :    Tuple							       =stop_memory_tracing(a   )
          if memory is None:
           snake_case__  :    Any							       =summary.total
         else:
          snake_case__  :    List[Any]							       =None

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













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





