


"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


A	:  List[Any]            =     logging.get_logger(__name__)
@dataclass
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =[
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]

							def __init__(	self       ,  **__a      ):
										for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																__lowerCAmelCase							       =     deprecated_arg[3:]
																__lowerCAmelCase							       =     not kwargs.pop(__a      )
																logger.warning(
																    f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
																    f" {positive_arg}={kwargs[positive_arg]}"      )
										__lowerCAmelCase							       =     kwargs.pop("tpu_name"       ,  self.tpu_name      )
										__lowerCAmelCase							       =     kwargs.pop("device_idx"       ,  self.device_idx      )
										__lowerCAmelCase							       =     kwargs.pop("eager_mode"       ,  self.eager_mode      )
										__lowerCAmelCase							       =     kwargs.pop("use_xla"       ,  self.use_xla      )
										super().__init__(**__a      )

							__UpperCAmelCase		:       str							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Name of TPU"""}			,)
							__UpperCAmelCase		:       int							 =field(
							    default=0			,metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}			,)
							__UpperCAmelCase		:       bool							 =field(default=lowerCAmelCase__			,metadata={"""help""": """Benchmark models in eager model."""}						)
							__UpperCAmelCase		:       bool							 =field(
							    default=lowerCAmelCase__			,metadata={
							        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
							    }			,)

							@cached_property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										__lowerCAmelCase							       =     None
										if self.tpu:
													try:
																if self.tpu_name:
																			__lowerCAmelCase							       =     tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
																else:
																			__lowerCAmelCase							       =     tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																__lowerCAmelCase							       =     None
										return tpu

							@cached_property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										if self.is_tpu:
													tf.config.experimental_connect_to_cluster(self._setup_tpu      )
													tf.tpu.experimental.initialize_tpu_system(self._setup_tpu      )

													__lowerCAmelCase							       =     tf.distribute.TPUStrategy(self._setup_tpu      )
										else:
													# currently no multi gpu is allowed
													if self.is_gpu:
																# TODO: Currently only single GPU is supported
																tf.config.set_visible_devices(self.gpu_list[self.device_idx]       ,  "GPU"      )
																__lowerCAmelCase							       =     tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"      )
													else:
																tf.config.set_visible_devices([]       ,  "GPU"      )  # disable GPU
																__lowerCAmelCase							       =     tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"      )

										return strategy

							@property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										return self._setup_tpu is not None

							@property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										return self._setup_strategy

							@property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										return tf.config.list_physical_devices("GPU"      )

							@property
							def      snake_case							(	self      ):
										requires_backends(self       ,  ["tf"]      )
										if self.cuda:
													return len(self.gpu_list      )
										return 0



							@property
							def      snake_case							(	self      ):
										return self.n_gpu > 0








"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     str(id_      )
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     {}  # {vertex:distance}

							def __lt__(	self       ,  __a      ):
										return self.key < other.key

							def __repr__(	self      ):
										return self.id

							def      snake_case							(	self       ,  __a      ):
										self.neighbors.append(__a      )



							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     weight


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			graph[a - 1].add_neighbor(graph[b - 1] )
			graph[b - 1].add_neighbor(graph[a - 1] )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]       ,					_UpperCamelCase )
			graph[b - 1].add_edge(graph[a - 1]       ,					_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     []
			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0
			__lowerCAmelCase							       =     graph[:]
			while q:
						__lowerCAmelCase							       =     min(_UpperCamelCase )
						q.remove(_UpperCamelCase )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
			for i in range(1       ,					len(_UpperCamelCase ) ):
						a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
			return a


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0

			__lowerCAmelCase							       =     list(_UpperCamelCase )
			hq.heapify(_UpperCamelCase )

			while h:
						__lowerCAmelCase							       =     hq.heappop(_UpperCamelCase )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
												hq.heapify(_UpperCamelCase )

			for i in range(1       ,					len(_UpperCamelCase ) ):
						yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def _lowerCamelCase      (						):

			'''simple docstring'''






if __name__ == "__main__":
	import doctest

	doctest.testmod()




