





'''simple docstring'''





import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, 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 ,						*__snake_case ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case ,						**__snake_case				)
							_snake_case       :					str				       =   eval_examples
							_snake_case       :					int				       =   post_process_function







		def        lowercase       (    self ,						__snake_case = None ,						__snake_case=None ,						__snake_case = None ,						__snake_case = "eval" ,						**__snake_case ,						)      ->      Dict[str, float]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   gen_kwargs.copy()
							_snake_case       :					Optional[int]				       =   (
							    gen_kwargs["max_length"] if gen_kwargs.get("max_length"				) is not None else self.args.generation_max_length
							)
							_snake_case       :					Optional[int]				       =   (
							    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"				) is not None else self.args.generation_num_beams
							)
							_snake_case       :					str				       =   gen_kwargs

							_snake_case       :					Union[str, Any]				       =   self.eval_dataset if eval_dataset is None else eval_dataset
							_snake_case       :					Any				       =   self.get_eval_dataloader(__snake_case				)
							_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       :					Dict				       =   None
							_snake_case       :					Optional[Any]				       =   time.time()
							_snake_case       :					List[str]				       =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
												_snake_case       :					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       :					List[str]				       =   compute_metrics
							_snake_case       :					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(
							        __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       :					int				       =   self.post_process_function(__snake_case ,						__snake_case ,						__snake_case				)
												_snake_case       :					Union[str, 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       :					Tuple				       =   metrics.pop(__snake_case				)

												metrics.update(output.metrics				)
							else:
												_snake_case       :					Tuple				       =   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       :					str				       =   self.callback_handler.on_evaluate(self.args ,						self.state ,						self.control ,						__snake_case				)
							return metrics







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case=None ,						__snake_case = "test" ,						**__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   gen_kwargs.copy()

							_snake_case       :					Tuple				       =   self.get_test_dataloader(__snake_case				)

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






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()