



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  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,*__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase=None	,**__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						super().__init__(*__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  eval_examples
						lowerCAmelCase__  :       List[str]				=  post_process_function



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  gen_kwargs.copy()
						lowerCAmelCase__  :       List[str]				=  (
						    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''				) is not None else self.args.generation_max_length
						)
						lowerCAmelCase__  :       Any				=  (
						    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''				) is not None else self.args.generation_num_beams
						)
						lowerCAmelCase__  :       int				=  gen_kwargs

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

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()				):
														if not key.startswith(f"""{metric_key_prefix}_"""				):
																		lowerCAmelCase__  :       Any				=  metrics.pop(__lowerCamelCase				)

										metrics.update(output.metrics				)
						else:
										lowerCAmelCase__  :       str				=  output.metrics

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

						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()				)

						lowerCAmelCase__  :       List[Any]				=  self.callback_handler.on_evaluate(self.args	,self.state	,self.control	,__lowerCamelCase				)
						return metrics



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase = "test"	,**__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  gen_kwargs.copy()

						lowerCAmelCase__  :       Tuple				=  self.get_test_dataloader(__lowerCamelCase				)

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

						lowerCAmelCase__  :       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(
						        __lowerCamelCase	,__lowerCamelCase	,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

						lowerCAmelCase__  :       List[str]				=  self.post_process_function(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,'''predict'''				)
						lowerCAmelCase__  :       Dict				=  self.compute_metrics(__lowerCamelCase				)

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






from collections.abc import Callable

import numpy as np






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Callable ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  int(np.ceil((x_end - xa) / step_size))
				lowerCAmelCase__  :       Any				=  np.zeros((n + 1,))
				lowerCAmelCase__  :       Union[str, Any]				=  ya
				lowerCAmelCase__  :       str				=  xa

				for k in range(lowerCamelCase_):
								lowerCAmelCase__  :       Tuple				=  y[k] + step_size * ode_func(lowerCamelCase_ ,y[k])
								lowerCAmelCase__  :       str				=  y[k] + (
								    (step_size / 2) * (ode_func(lowerCamelCase_ ,y[k]) + ode_func(x + step_size ,lowerCamelCase_))
								)
								x += step_size

				return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()

