


'''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 		lowerCAmelCase__	(	lowercase_		):




			def __init__(						self     :   List[str]		,     *lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=None		,     lowerCamelCase__     :   Any=None		,     **lowerCamelCase__     :   Dict       )      ->Tuple:



					'''simple docstring'''







					super().__init__(*lowerCamelCase__		,     **lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					eval_examples
					_UpperCAmelCase						:				Union[str, Any]	    =					post_process_function




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   str = None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Tuple = None		,     lowerCamelCase__     :   Tuple = "eval"		,     **lowerCamelCase__     :   int		,     )      ->Dict:



					'''simple docstring'''







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

					_UpperCAmelCase						:				List[str]	    =					self.eval_dataset if eval_dataset is None else eval_dataset
					_UpperCAmelCase						:				Tuple	    =					self.get_eval_dataloader(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					self.eval_examples if eval_examples is None else eval_examples

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

							metrics.update(output.metrics       )
					else:
							_UpperCAmelCase						:				Union[str, Any]	    =					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()       )

					_UpperCAmelCase						:				Optional[int]	    =					self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     lowerCamelCase__       )
					return metrics






			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Dict		,     lowerCamelCase__     :   str=None		,     lowerCamelCase__     :   Tuple = "test"		,     **lowerCamelCase__     :   Tuple       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					gen_kwargs.copy()

					_UpperCAmelCase						:				Tuple	    =					self.get_test_dataloader(lowerCamelCase__       )

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

					_UpperCAmelCase						:				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(
					        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

					_UpperCAmelCase						:				List[Any]	    =					self.post_process_function(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     "predict"       )
					_UpperCAmelCase						:				str	    =					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}_"""       ):
									_UpperCAmelCase						:				str	    =					metrics.pop(lowerCamelCase__       )
					metrics.update(output.metrics       )
					return PredictionOutput(predictions=predictions.predictions		,     label_ids=predictions.label_ids		,     metrics=lowerCamelCase__       )






from __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




