



"""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  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							Optional[int]						,     *snake_case:							Union[str, Any]						,     snake_case:							Dict=None						,     snake_case:							Optional[Any]=None						,     **snake_case:							Tuple						)   ->	str:
											super().__init__(*snake_case						,     **snake_case						)
											snake_case_ :List[Any]						=			eval_examples
											snake_case_ :Optional[Any]						=			post_process_function






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							str=None						,     snake_case:							List[Any]=None						,     snake_case:							Tuple=None						,     snake_case:							str = "eval"						)   ->	Union[str, Any]:
											snake_case_ :Dict						=			self.eval_dataset if eval_dataset is None else eval_dataset
											snake_case_ :str						=			self.get_eval_dataloader(snake_case						)
											snake_case_ :Union[str, Any]						=			self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											snake_case_ :str						=			self.compute_metrics
											snake_case_ :List[str]						=			None
											snake_case_ :Any						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											snake_case_ :Optional[int]						=			time.time()
											try:
																	snake_case_ :Optional[Any]						=			eval_loop(
																	    snake_case						,     description="""Evaluation"""						,     prediction_loss_only=True if compute_metrics is None else None						,     ignore_keys=snake_case						,     metric_key_prefix=snake_case						,     )
											finally:
																	snake_case_ :Optional[int]						=			compute_metrics
											snake_case_ :Dict						=			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(
											        snake_case						,     snake_case						,     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
																	snake_case_ :str						=			self.post_process_function(snake_case						,     snake_case						,     output.predictions						)
																	snake_case_ :str						=			self.compute_metrics(snake_case						)

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

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

											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()						)

											snake_case_ :Union[str, Any]						=			self.callback_handler.on_evaluate(self.args						,     self.state						,     self.control						,     snake_case						)
											return metrics






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Optional[Any]						,     snake_case:							int						,     snake_case:							Optional[Any]=None						,     snake_case:							str = "test"						)   ->	Dict:
											snake_case_ :List[str]						=			self.get_test_dataloader(snake_case						)

											# Temporarily disable metric computation, we will do it in the loop here.
											snake_case_ :Optional[Any]						=			self.compute_metrics
											snake_case_ :int						=			None
											snake_case_ :List[Any]						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											snake_case_ :Optional[Any]						=			time.time()
											try:
																	snake_case_ :Dict						=			eval_loop(
																	    snake_case						,     description="""Prediction"""						,     prediction_loss_only=True if compute_metrics is None else None						,     ignore_keys=snake_case						,     metric_key_prefix=snake_case						,     )
											finally:
																	snake_case_ :Any						=			compute_metrics
											snake_case_ :int						=			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(
											        snake_case						,     snake_case						,     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

											snake_case_ :Optional[Any]						=			self.post_process_function(snake_case						,     snake_case						,     output.predictions						,     """predict"""						)
											snake_case_ :Optional[Any]						=			self.compute_metrics(snake_case						)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()						):
																	if not key.startswith(f"""{metric_key_prefix}_"""						):
																							snake_case_ :Union[str, Any]						=			metrics.pop(snake_case						)
											metrics.update(output.metrics						)
											return PredictionOutput(predictions=predictions.predictions						,     label_ids=predictions.label_ids						,     metrics=snake_case						)





"""simple docstring"""
from __future__ import annotations

from collections import Counter
from random import random
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Tuple						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			{}






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						)   ->	None:
											snake_case_ :str						=			{}






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							str						,     snake_case:							float						)   ->	None:
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											snake_case_ :Dict						=			probability






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	list[str]:
											return list(self.connections						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							str						)   ->	str:
											snake_case_ :Optional[Any]						=			0
											snake_case_ :List[str]						=			random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(_lowercase,					_lowercase,					_lowercase	)

						snake_case_ :int						=			Counter(graph.get_nodes()	)
						snake_case_ :Optional[Any]						=			start

						for _ in range(_lowercase	):
												snake_case_ :Tuple						=			graph.transition(_lowercase	)
												visited[node] += 1

						return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
