



"""simple docstring"""







import heapq
import sys

import numpy as np

lowercase__		:    Optional[int]				   =     tuple[int, int]







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Any	):
        lowerCAmelCase_       :    Optional[int]								=			[]
        lowerCAmelCase_       :    Dict								=			set()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        if not self.empty():
          return self.elements[0][0]
        else:
          return float('inf'	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return len(self.elements	) == 0



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]	):
        if item not in self.set:
          heapq.heappush(self.elements   ,      (priority, item)	)
          self.set.add(SCREAMING_SNAKE_CASE_	)
        else:
          # update
          # print("update", item)
          lowerCAmelCase_       :    Dict								=			[]
          ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    List[Any]								=			heapq.heappop(self.elements	)
          while x != item:
            temp.append((pri, x)	)
            ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    List[str]								=			heapq.heappop(self.elements	)
          temp.append((priority, item)	)
          for pro, xxx in temp:
            heapq.heappush(self.elements   ,      (pro, xxx)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        if item in self.set:
          self.set.remove(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    str								=			[]
          ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    Optional[int]								=			heapq.heappop(self.elements	)
          while x != item:
            temp.append((pro, x)	)
            ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    Optional[int]								=			heapq.heappop(self.elements	)
          for prito, yyy in temp:
            heapq.heappush(self.elements   ,      (prito, yyy)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        return self.elements[0][1]





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    str								=			heapq.heappop(self.elements	)
        self.set.remove(SCREAMING_SNAKE_CASE_	)
        return (priority, item)




def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    TPos						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			np.array(lowerCAmelCase__						)
  lowerCAmelCase_       :    Dict								=			np.array(lowerCAmelCase__						)
  return np.linalg.norm(a - b						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    TPos						)						->       Union[str, Any]:

  """simple docstring"""




  return consistent_heuristic(lowerCAmelCase__   ,       lowerCAmelCase__						) // t

def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    TPos						)						->       List[Any]:

  """simple docstring"""




  return abs(p[0] - goal[0]						) + abs(p[1] - goal[1]						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    dict[TPos, float]						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[int]								=			g_function[start] + Wa * heuristics[i](lowerCAmelCase__   ,       lowerCAmelCase__						)
  return ans

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]						)						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			np.chararray((n, n)						)
  for i in range(lowerCAmelCase__						):
    for j in range(lowerCAmelCase__						):
      lowerCAmelCase_       :    Tuple								=			'*'

  for i in range(lowerCAmelCase__						):
    for j in range(lowerCAmelCase__						):
      if (j, (n - 1) - i) in blocks:
        lowerCAmelCase_       :    str								=			'#'

  lowerCAmelCase_       :    List[str]								=			'-'
  lowerCAmelCase_       :    Optional[int]								=			back_pointer[goal]
  while x != start:
    ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    int								=			x
    # print(x)
    lowerCAmelCase_       :    Tuple								=			'-'
    lowerCAmelCase_       :    List[str]								=			back_pointer[x]
  lowerCAmelCase_       :    Optional[int]								=			'-'

  for i in range(lowerCAmelCase__						):
    for j in range(lowerCAmelCase__						):
      if (i, j) == (0, n - 1):
        print(grid[i][j]   ,       end=' '						)
        print('<-- End position'   ,       end=' '						)
      else:
        print(grid[i][j]   ,       end=' '						)
    print()
  print('^'						)
  print('Start position'						)
  print()
  print('# is an obstacle'						)
  print('- is the path taken by algorithm'						)
  print('PATH TAKEN BY THE ALGORITHM IS:-'						)
  lowerCAmelCase_       :    Any								=			back_pointer[goal]
  while x != start:
    print(lowerCAmelCase__   ,       end=' '						)
    lowerCAmelCase_       :    Optional[Any]								=			back_pointer[x]
  print(lowerCAmelCase__						)
  sys.exit()

def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos						)						->       str:

  """simple docstring"""




  if p[0] < 0 or p[0] > n - 1:
    return False
  if p[1] < 0 or p[1] > n - 1:
    return False
  return True

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Any   ,       )						->       Optional[Any]:

  """simple docstring"""




  for itera in range(lowerCAmelCase__						):
    open_list[itera].remove_element(lowerCAmelCase__						)
  # print("s", s)
  # print("j", j)
  ((lowerCAmelCase_)						,(lowerCAmelCase_))       :    int								=			s
  lowerCAmelCase_       :    List[str]								=			(x - 1, y)
  lowerCAmelCase_       :    Optional[Any]								=			(x + 1, y)
  lowerCAmelCase_       :    List[str]								=			(x, y + 1)
  lowerCAmelCase_       :    Dict								=			(x, y - 1)

  for neighbours in [left, right, up, down]:
    if neighbours not in blocks:
      if valid(lowerCAmelCase__						) and neighbours not in visited:
        # print("neighbour", neighbours)
        visited.add(lowerCAmelCase__						)
        lowerCAmelCase_       :    List[str]								=			-1
        lowerCAmelCase_       :    Union[str, Any]								=			float('inf'						)

      if valid(lowerCAmelCase__						) and g_function[neighbours] > g_function[s] + 1:
        lowerCAmelCase_       :    Optional[Any]								=			g_function[s] + 1
        lowerCAmelCase_       :    Union[str, Any]								=			s
        if neighbours not in close_list_anchor:
          open_list[0].put(lowerCAmelCase__   ,       key(lowerCAmelCase__   ,       0   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)						)
          if neighbours not in close_list_inad:
            for var in range(1   ,       lowerCAmelCase__						):
              if key(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						) <= Wa * key(
                  lowerCAmelCase__   ,       0   ,       lowerCAmelCase__   ,       lowerCAmelCase__						):
                open_list[j].put(
                    lowerCAmelCase__   ,       key(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)						)

def     UpperCamelCase_   (   )						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			[]
  for x in range(1   ,       5						):
    for y in range(1   ,       6						):
      some_list.append((x, y)						)

  for x in range(15   ,       20						):
    some_list.append((x, 17)						)

  for x in range(10   ,       19						):
    for y in range(1   ,       15						):
      some_list.append((x, y)						)

    # L block
  for x in range(1   ,       4						):
    for y in range(12   ,       19						):
      some_list.append((x, y)						)
  for x in range(3   ,       13						):
    for y in range(16   ,       19						):
      some_list.append((x, y)						)
  return some_list


lowercase__		:    str				   =     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowercase__		:    Tuple				   =     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (1_0, 1),
    (1_1, 1),
    (1_2, 1),
    (1_3, 1),
    (1_4, 1),
    (1_5, 1),
    (1_6, 1),
    (1_7, 1),
    (1_8, 1),
    (1_9, 1),
]
lowercase__		:    Optional[Any]				   =     make_common_ground()


lowercase__		:    Any				   =     blocks_blk
# hyper parameters
lowercase__		:    List[Any]				   =     1
lowercase__		:    Optional[int]				   =     1
lowercase__		:    Optional[Any]				   =     2_0
lowercase__		:    Dict				   =     3  # one consistent and two other inconsistent

# start and end destination
lowercase__		:    Optional[Any]				   =     (0, 0)
lowercase__		:    str				   =     (n - 1, n - 1)

lowercase__		:    str				   =     1

def     UpperCamelCase_   (   lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    TPos   ,       lowerCAmelCase__				:    int						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			{start: 0, goal: float('inf'						)}
  lowerCAmelCase_       :    Optional[Any]								=			{start: -1, goal: -1}
  lowerCAmelCase_       :    Optional[Any]								=			[]
  lowerCAmelCase_       :    Union[str, Any]								=			set()

  for i in range(lowerCAmelCase__						):
    open_list.append(PriorityQueue()						)
    open_list[i].put(lowerCAmelCase__   ,       key(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)						)

  lowerCAmelCase_       :    list[int]								=			[]
  lowerCAmelCase_       :    list[int]								=			[]
  while open_list[0].minkey() < float('inf'						):
    for i in range(1   ,       lowerCAmelCase__						):
      # print(open_list[0].minkey(), open_list[i].minkey())
      if open_list[i].minkey() <= Wa * open_list[0].minkey():
        global t
        t += 1
        if g_function[goal] <= open_list[i].minkey():
          if g_function[goal] < float('inf'						):
            do_something(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
        else:
          lowerCAmelCase_						,lowerCAmelCase_       :    Union[str, Any]								=			open_list[i].top_show()
          visited.add(lowerCAmelCase__						)
          expand_state(
              lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       )
          close_list_inad.append(lowerCAmelCase__						)
      else:
        if g_function[goal] <= open_list[0].minkey():
          if g_function[goal] < float('inf'						):
            do_something(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
        else:
          lowerCAmelCase_       :    Any								=			open_list[0].top_show()
          visited.add(lowerCAmelCase__						)
          expand_state(
              lowerCAmelCase__   ,       0   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       )
          close_list_anchor.append(lowerCAmelCase__						)
  print('No path found to goal'						)
  print()
  for i in range(n - 1   ,       -1   ,       -1						):
    for j in range(lowerCAmelCase__						):
      if (j, i) in blocks:
        print('#'   ,       end=' '						)
      elif (j, i) in back_pointer:
        if (j, i) == (n - 1, n - 1):
          print('*'   ,       end=' '						)
        else:
          print('-'   ,       end=' '						)
      else:
        print('*'   ,       end=' '						)
      if (j, i) == (n - 1, n - 1):
        print('<-- End position'   ,       end=' '						)
    print()
  print('^'						)
  print('Start position'						)
  print()
  print('# is an obstacle'						)
  print('- is the path taken by algorithm'						)


if __name__ == "__main__":
     multi_a_star(start, goal, n_heuristic)










"""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

lowercase__		:    str				   =     logging.get_logger(__name__)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bool   ,       lowerCAmelCase__				:    bool						)						->       List[Any]:

  """simple docstring"""




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

    @wraps(lowerCAmelCase__						)
    @tf.function(experimental_compile=lowerCAmelCase__						)
    def run_in_graph_mode(*lowerCAmelCase__				:    Dict   ,       **lowerCAmelCase__				:    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__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int						)						->       ["tf.Tensor"]:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			random.Random()
  lowerCAmelCase_       :    List[str]								=			[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_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					42
      _SCREAMING_SNAKE_CASE      =					42
      _SCREAMING_SNAKE_CASE      =					"TensorFlow"



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        return tf.__version__



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



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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        # initialize GPU on separate process
        if self.args.is_gpu:
          tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Any								=			self.args.strategy
        if strategy is None:
          raise ValueError('A device strategy has to be initialized before using TensorFlow.'	)
        lowerCAmelCase_       :    Optional[Any]								=			self._prepare_inference_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_memory(_inference	)



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

        lowerCAmelCase_       :    Optional[int]								=			self._prepare_train_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_memory(_train	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    Any								=			self.config_dict[model_name]

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

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

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

        lowerCAmelCase_       :    List[Any]								=			encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

        return _inference



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    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.'	)

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

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase_       :    int								=			config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_   ,      'vocab_size'	) else config.encoder.vocab_size
        lowerCAmelCase_       :    Optional[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_train():
          lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      decoder_input_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)[0]
          lowerCAmelCase_       :    Optional[int]								=			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():
          lowerCAmelCase_       :    Dict								=			model(SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)[0]
          lowerCAmelCase_       :    str								=			tf.gradients(SCREAMING_SNAKE_CASE_   ,      model.trainable_variables	)
          return gradients

        lowerCAmelCase_       :    int								=			encoder_decoder_train if config.is_encoder_decoder else encoder_train

        return _train



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        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
            lowerCAmelCase_       :    Dict								=			timeit.repeat(
                SCREAMING_SNAKE_CASE_   ,      repeat=self.args.repeat   ,      number=1_0   ,      )

            return min(SCREAMING_SNAKE_CASE_	) / 10.0
          except ResourceExhaustedError as e:
            self.print_fn(F"Doesn't fit on GPU. {e}"	)





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

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





