




'''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 			_a  (	__a ):
       def __init__(	self						:	Optional[int]   ,				*lowercase						:	Optional[Any]   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Tuple=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           super().__init__(*lowercase   ,				**lowercase				)
           UpperCAmelCase			       =		eval_examples
           UpperCAmelCase			       =		post_process_function
       def     A      (	self						:	str   ,				lowercase						:	List[Any]=None   ,				lowercase						:	Tuple=None   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str = "eval"				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.eval_dataset if eval_dataset is None else eval_dataset
           UpperCAmelCase			       =		self.get_eval_dataloader(lowercase				)
           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(
                   lowercase   ,				description='''Evaluation'''   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowercase   ,				metric_key_prefix=lowercase   ,				)
           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(
                   lowercase   ,				lowercase   ,				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(lowercase   ,				lowercase   ,				output.predictions				)
               UpperCAmelCase			       =		self.compute_metrics(lowercase				)

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

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

           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   ,				lowercase				)
           return metrics







       def     A      (	self						:	Optional[Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str = "test"				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_test_dataloader(lowercase				)

           # 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(
                   lowercase   ,				description='''Prediction'''   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowercase   ,				metric_key_prefix=lowercase   ,				)
           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(
                   lowercase   ,				lowercase   ,				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(lowercase   ,				lowercase   ,				output.predictions   ,				'''predict'''				)
           UpperCAmelCase			       =		self.compute_metrics(lowercase				)

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










'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





def 						snake_case_							(_a	:							dict    ,   _a	:							str    ,   _a	:							set    ,   _a	:							set    ,   _a	:							dict    ,   _a	:							dict    ,   _a	:							PriorityQueue    ,   _a	:							dict    ,   _a	:							float | int    ,   ):
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        UpperCAmelCase			       =		cst_fwd.get(_a    ,   np.inf     )
        UpperCAmelCase			       =		cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt)     )
            UpperCAmelCase			       =		new_cost_f
            UpperCAmelCase			       =		v
        if nxt in visited_backward:
            if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                UpperCAmelCase			       =		cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		{source: 0}
    UpperCAmelCase			       =		{destination: 0}
    UpperCAmelCase			       =		{source: None}
    UpperCAmelCase			       =		{destination: None}
    UpperCAmelCase			       =		PriorityQueue()
    UpperCAmelCase			       =		PriorityQueue()

    UpperCAmelCase			       =		np.inf

    queue_forward.put((0, source)     )
    queue_backward.put((0, destination)     )

    if source == destination:
        return 0

    while not queue_forward.empty() and not queue_backward.empty():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
            break

    if shortest_distance != np.inf:
        UpperCAmelCase			       =		shortest_distance
    return shortest_path_distance


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()




