





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 : List[str]						,				*_A : List[str]						,				_A : List[str]=None						,				_A : int=None						,				**_A : str		)							->      Any:







							"""simple docstring"""


							super().__init__(*_A						,				**_A		)
							snake_case_						:     Optional[Any]     =		eval_examples
							snake_case_						:     Tuple     =		post_process_function






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







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.eval_dataset if eval_dataset is None else eval_dataset
							snake_case_						:     Optional[Any]     =		self.get_eval_dataloader(_A		)
							snake_case_						:     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_						:     List[str]     =		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_						:     Optional[Any]     =		time.time()
							try:
								snake_case_						:     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_						:     int     =		compute_metrics
							snake_case_						:     Tuple     =		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_						:     Dict     =		self.post_process_function(_A						,				_A						,				output.predictions		)
								snake_case_						:     Optional[int]     =		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_						:     Any     =		metrics.pop(_A		)
								metrics.update(output.metrics		)
							else:
								snake_case_						:     List[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_						:     Dict     =		self.callback_handler.on_evaluate(self.args						,				self.state						,				self.control						,				_A		)
							return metrics






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







							"""simple docstring"""


							snake_case_						:     Any     =		self.get_test_dataloader(_A		)

							# Temporarily disable metric computation, we will do it in the loop here.
							snake_case_						:     Tuple     =		self.compute_metrics
							snake_case_						:     List[str]     =		None
							snake_case_						:     List[str]     =		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							snake_case_						:     Optional[int]     =		time.time()
							try:
								snake_case_						:     Union[str, Any]     =		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_						:     Tuple     =		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_						:     Dict     =		self.post_process_function(_A						,				_A						,				output.predictions						,				'predict'		)
							snake_case_						:     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_						:     Dict     =		metrics.pop(_A		)
							metrics.update(output.metrics		)
							return PredictionOutput(predictions=predictions.predictions						,				label_ids=predictions.label_ids						,				metrics=_A		)








def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = False				):

	if not isinstance(__a				,	__a				):
		snake_case_						:     str     =		f"""Expected string as input, found {type(__a				)}"""
		raise ValueError(__a				)
	if not isinstance(__a				,	__a				):
		snake_case_						:     int     =		f"""Expected boolean as use_pascal parameter, found {type(__a				)}"""
		raise ValueError(__a				)

	snake_case_						:     Union[str, Any]     =		input_str.split('_'				)

	snake_case_						:     int     =		0 if use_pascal else 1

	snake_case_						:     List[Any]     =		words[start_index:]

	snake_case_						:     str     =		[word[0].upper() + word[1:] for word in words_to_capitalize]

	snake_case_						:     Optional[Any]     =		'' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
						from doctest import testmod

						testmod()

