





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 SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Dict		,   *_A      :      List[Any]		,   _A      :      List[Any]=None		,   _A      :      Optional[int]=None		,   **_A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							super().__init__(*_A		,   **_A	)
							snake_case_		:			Dict											=	eval_examples
							snake_case_		:			Union[str, Any]											=	post_process_function






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[Any]=None		,   _A      :      Any=None		,   _A      :      List[str]=None		,   _A      :      str = "eval"	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			int											=	self.eval_dataset if eval_dataset is None else eval_dataset
							snake_case_		:			Union[str, Any]											=	self.get_eval_dataloader(_A	)
							snake_case_		:			List[str]											=	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_		:			Any											=	self.compute_metrics
							snake_case_		:			List[str]											=	None
							snake_case_		:			int											=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							snake_case_		:			List[str]											=	time.time()
							try:
								snake_case_		:			Optional[Any]											=	eval_loop(
								    _A		,   description='Evaluation'		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=_A		,   metric_key_prefix=_A		,   )
							finally:
								snake_case_		:			Tuple											=	compute_metrics
							snake_case_		:			List[str]											=	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(
							        _A		,   _A		,   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_		:			Tuple											=	self.post_process_function(_A		,   _A		,   output.predictions	)
								snake_case_		:			List[str]											=	self.compute_metrics(_A	)

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()	):
									if not key.startswith(F"""{metric_key_prefix}_"""	):
										snake_case_		:			Dict											=	metrics.pop(_A	)
								metrics.update(output.metrics	)
							else:
								snake_case_		:			Union[str, Any]											=	output.metrics

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

							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_		:			Tuple											=	self.callback_handler.on_evaluate(self.args		,   self.state		,   self.control		,   _A	)
							return metrics






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      Optional[int]		,   _A      :      str		,   _A      :      Optional[int]=None		,   _A      :      str = "test"	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.get_test_dataloader(_A	)

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

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








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

