





'''simple docstring'''

import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[Any]			,					*lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Any=None			,					lowerCAmelCase__  :						Tuple=None			,					**lowerCAmelCase__  :						Any	)       ->	List[str]:

            '''simple docstring'''




            super().__init__(*lowerCAmelCase__			,					**lowerCAmelCase__	)
            _UpperCamelCase							       =			eval_examples
            _UpperCamelCase							       =			post_process_function





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Tuple=None			,					lowerCAmelCase__  :						Any=None			,					lowerCAmelCase__  :						str=None			,					lowerCAmelCase__  :						str = "eval"	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.eval_dataset if eval_dataset is None else eval_dataset
            _UpperCamelCase							       =			self.get_eval_dataloader(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.eval_examples if eval_examples is None else eval_examples

            # Temporarily disable metric computation, we will do it in the loop here.
            _UpperCamelCase							       =			self.compute_metrics
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            _UpperCamelCase							       =			time.time()
            try:
                 _UpperCamelCase							       =			eval_loop(
                     lowerCAmelCase__			,					description='''Evaluation'''			,					prediction_loss_only=True if compute_metrics is None else None			,					ignore_keys=lowerCAmelCase__			,					metric_key_prefix=lowerCAmelCase__			,					)
            finally:
                 _UpperCamelCase							       =			compute_metrics
            _UpperCamelCase							       =			self.args.eval_batch_size * self.args.world_size
            if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
                 start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
            output.metrics.update(
                speed_metrics(
                    lowerCAmelCase__			,					lowerCAmelCase__			,					num_samples=output.num_samples			,					num_steps=math.ceil(output.num_samples / total_batch_size	)			,					)	)
            if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                 # Only the main node write the results by default
                 _UpperCamelCase							       =			self.post_process_function(lowerCAmelCase__			,					lowerCAmelCase__			,					output.predictions	)
                 _UpperCamelCase							       =			self.compute_metrics(lowerCAmelCase__	)

                 # Prefix all keys with metric_key_prefix + '_'
                 for key in list(metrics.keys()	):
                      if not key.startswith(f"""{metric_key_prefix}_"""	):
                           _UpperCamelCase							       =			metrics.pop(lowerCAmelCase__	)
                 metrics.update(output.metrics	)
            else:
                 _UpperCamelCase							       =			output.metrics

            if self.args.should_log:
                 # Only the main node log the results by default
                 self.log(lowerCAmelCase__	)

            if self.args.tpu_metrics_debug or self.args.debug:
                 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                 xm.master_print(met.metrics_report()	)

            _UpperCamelCase							       =			self.callback_handler.on_evaluate(self.args			,					self.state			,					self.control			,					lowerCAmelCase__	)
            return metrics







       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						int=None			,					lowerCAmelCase__  :						str = "test"	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_test_dataloader(lowerCAmelCase__	)

            # Temporarily disable metric computation, we will do it in the loop here.
            _UpperCamelCase							       =			self.compute_metrics
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            _UpperCamelCase							       =			time.time()
            try:
                 _UpperCamelCase							       =			eval_loop(
                     lowerCAmelCase__			,					description='''Prediction'''			,					prediction_loss_only=True if compute_metrics is None else None			,					ignore_keys=lowerCAmelCase__			,					metric_key_prefix=lowerCAmelCase__			,					)
            finally:
                 _UpperCamelCase							       =			compute_metrics
            _UpperCamelCase							       =			self.args.eval_batch_size * self.args.world_size
            if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
                 start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
            output.metrics.update(
                speed_metrics(
                    lowerCAmelCase__			,					lowerCAmelCase__			,					num_samples=output.num_samples			,					num_steps=math.ceil(output.num_samples / total_batch_size	)			,					)	)

            if self.post_process_function is None or self.compute_metrics is None:
                 return output

            _UpperCamelCase							       =			self.post_process_function(lowerCAmelCase__			,					lowerCAmelCase__			,					output.predictions			,					'''predict'''	)
            _UpperCamelCase							       =			self.compute_metrics(lowerCAmelCase__	)

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()	):
                 if not key.startswith(f"""{metric_key_prefix}_"""	):
                      _UpperCamelCase							       =			metrics.pop(lowerCAmelCase__	)
            metrics.update(output.metrics	)
            return PredictionOutput(predictions=predictions.predictions			,					label_ids=predictions.label_ids			,					metrics=lowerCAmelCase__	)







"""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()




