
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

lowercase__          =logging.get_logger(__name__)




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

		@wraps(lowerCAmelCase__  )
		@tf.function(experimental_compile=lowerCAmelCase__  )
		def run_in_graph_mode(*lowerCAmelCase__							:    List[str]  ,       **lowerCAmelCase__							:    Union[str, Any]  ):
			return func(*lowerCAmelCase__  ,       **lowerCAmelCase__  )

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



class 		UpperCamelCase__  (	__lowercase   ):
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				42
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				42
			_SCREAMING_SNAKE_CASE				:							List[str]     	=				"TensorFlow"
			@property
			def 				lowerCAmelCase      (self			: List[Any]       ):
				return tf.__version__
			def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: List[Any] ,    snake_case_			: Dict ,    snake_case_			: Any       ):
				__a			:  Tuple		    =  self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''       )
				__a			:  List[str]		    =  self._prepare_inference_func(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
				return self._measure_speed(_inference       )
			def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: Union[str, Any] ,    snake_case_			: Any ,    snake_case_			: List[str]       ):
				__a			:  Optional[Any]		    =  self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''       )
				__a			:  Any		    =  self._prepare_train_func(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
				return self._measure_speed(_train       )
			def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: str ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str]       ):





				if self.args.is_gpu:
					tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,    _SCREAMING_SNAKE_CASE       )
				__a			:  Tuple		    =  self.args.strategy
				if strategy is None:
					raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''       )
				__a			:  int		    =  self._prepare_inference_func(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
				return self._measure_memory(_inference       )
			def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: int ,    snake_case_			: Optional[Any] ,    snake_case_			: Any       ):





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

				__a			:  Optional[int]		    =  self._prepare_train_func(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
				return self._measure_memory(_train       )
			def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Dict ,    snake_case_			: List[Any] ,    snake_case_			: Dict       ):
				__a			:  str		    =  self.config_dict[model_name]

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

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

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

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

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

				__a			:  Union[str, Any]		    =  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

				return _inference
			def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: Tuple ,    snake_case_			: Dict ,    snake_case_			: List[str]       ):
				__a			:  Tuple		    =  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(_SCREAMING_SNAKE_CASE ,    '''architectures'''       )
				    and isinstance(config.architectures ,    _SCREAMING_SNAKE_CASE       )
				    and len(config.architectures       ) > 0
				)
				if not self.args.only_pretrain_model and has_model_class_in_config:
					try:
						__a			:  List[Any]		    =  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
						__a			:  str		    =  __import__('''transformers''' ,    fromlist=[model_class]       )
						__a			:  Optional[int]		    =  getattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
						__a			:  Tuple		    =  model_cls(_SCREAMING_SNAKE_CASE       )
					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__](_SCREAMING_SNAKE_CASE       )

				# encoder-decoder has vocab size saved differently
				__a			:  Any		    =  config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE ,    '''vocab_size'''       ) else config.encoder.vocab_size
				__a			:  Tuple		    =  random_input_ids(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )

				@run_with_tf_optimizations(self.args.eager_mode ,    self.args.use_xla       )
				def encoder_decoder_train():
					__a			:  str		    =  model(_SCREAMING_SNAKE_CASE ,    decoder_input_ids=_SCREAMING_SNAKE_CASE ,    labels=_SCREAMING_SNAKE_CASE ,    training=_SCREAMING_SNAKE_CASE       )[0]
					__a			:  Optional[Any]		    =  tf.gradients(_SCREAMING_SNAKE_CASE ,    model.trainable_variables       )
					return gradients

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

				__a			:  Any		    =  encoder_decoder_train if config.is_encoder_decoder else encoder_train

				return _train
			def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[str]       ):





				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(_SCREAMING_SNAKE_CASE ,    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			:  str		    =  timeit.repeat(
						    _SCREAMING_SNAKE_CASE ,    repeat=self.args.repeat ,    number=1_0 ,    )

						return min(_SCREAMING_SNAKE_CASE       ) / 1_0.0
					except ResourceExhaustedError as e:
						self.print_fn(f"Doesn\'t fit on GPU. {e}"       )







			def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Tuple       ):
				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			:  str		    =  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			:  Union[str, 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()
								__a			:  Tuple		    =  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
								__a			:  int		    =  nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE       )
								__a			:  Union[str, Any]		    =  meminfo.used
								__a			:  Union[str, Any]		    =  Memory(_SCREAMING_SNAKE_CASE       )
								# 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			:  Union[str, Any]		    =  None
							else:
								__a			:  int		    =  measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE       )
								__a			:  Any		    =  Memory(_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       ) else memory_bytes
						if self.args.trace_memory_line_by_line:
							__a			:  List[str]		    =  stop_memory_tracing(_SCREAMING_SNAKE_CASE       )
							if memory is None:
								__a			:  Optional[int]		    =  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


def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):

 def merge(lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0  )
   yield from left
   yield from right

  return list(_merge()  )

 if len(lowerCAmelCase__  ) <= 1:
  return collection
 __a			:  str		    =  len(lowerCAmelCase__  ) // 2
 return merge(merge_sort(collection[:mid]  )  ,       merge_sort(collection[mid:]  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__          =input('Enter numbers separated by a comma:\n').strip()
      lowercase__          =[int(item) for item in user_input.split(',')]
      print(*merge_sort(unsorted), sep=',')
