




"""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__			(       UpperCamelCase_  ):





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




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	str     ,a__      :	Optional[Dataset] = None     ,a__      :	int=None     ,a__      :	Optional[List[str]] = None     ,a__      :	str = "eval"     ,**a__      :	Union[str, Any]     ,)						->       Dict[str, float]:




											"""simple docstring"""







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

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

																metrics.update(output.metrics)
											else:
																_lowerCAmelCase:Optional[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())

											_lowerCAmelCase:Any									=  self.callback_handler.on_evaluate(self.args     ,self.state     ,self.control     ,a__)
											return metrics







						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	int     ,a__      :	Tuple     ,a__      :	Optional[Any]=None     ,a__      :	str = "test"     ,**a__      :	Any)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  gen_kwargs.copy()

											_lowerCAmelCase:Dict									=  self.get_test_dataloader(a__)

											# Temporarily disable metric computation, we will do it in the loop here.
											_lowerCAmelCase:List[str]									=  self.compute_metrics
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:Tuple									=  time.time()
											_lowerCAmelCase:str									=  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																_lowerCAmelCase: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:
																_lowerCAmelCase:int									=  compute_metrics

											_lowerCAmelCase: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 None or self.compute_metrics is None:
																return output

											_lowerCAmelCase:Tuple									=  self.post_process_function(a__     ,a__     ,a__     ,'''predict''')
											_lowerCAmelCase: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}_'):
																					_lowerCAmelCase:Union[str, Any]									=  metrics.pop(a__)
											metrics.update(output.metrics)
											return PredictionOutput(predictions=predictions.predictions     ,label_ids=predictions.label_ids     ,metrics=a__)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					assert x is not None
					assert y is not None

					_lowerCAmelCase:Optional[Any]									=  len(snake_case    )
					_lowerCAmelCase:Tuple									=  len(snake_case    )

					# declaring the array for storing the dp values
					_lowerCAmelCase:Optional[Any]									=  [[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

					for i in range(1      ,       m + 1    ):
										for j in range(1      ,       n + 1    ):
															_lowerCAmelCase:Optional[int]									=  1 if x[i - 1] == y[j - 1] else 0

															_lowerCAmelCase:int									=  max(l[i - 1][j]      ,       l[i][j - 1]      ,       l[i - 1][j - 1] + match    )

					_lowerCAmelCase:List[Any]									=  ''''''
					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  m, n
					while i > 0 and j > 0:
										_lowerCAmelCase:Any									=  1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				_lowerCAmelCase:Any									=  x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
				UpperCamelCase__						=			'''AGGTAB'''
				UpperCamelCase__						=			'''GXTXAYB'''
				UpperCamelCase__						=			4
				UpperCamelCase__						=			'''GTAB'''

				UpperCamelCase__						,							UpperCamelCase__						=			longest_common_subsequence(a, b)
				print('''len =''', ln, ''', sub-sequence =''', subseq)
				import doctest

				doctest.testmod()






