



'''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   :						Optional[Any]      ,						*a_   :						Dict      ,						a_   :						List[str]=None      ,						a_   :						Dict=None      ,						**a_   :						int			)			->					List[str]:
										super().__init__(*lowerCamelCase__      ,						**lowerCamelCase__			)
										snake_case:    int      							=eval_examples
										snake_case:    int      							=post_process_function


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Optional[Dataset] = None      ,						a_   :						Any=None      ,						a_   :						Optional[List[str]] = None      ,						a_   :						str = "eval"      ,						**a_   :						Optional[Any]      ,						)			->					List[str]:
										snake_case:    Optional[int]      							=gen_kwargs.copy()
										snake_case:    List[str]      							=(
										    gen_kwargs["""max_length"""] if gen_kwargs.get('max_length'			) is not None else self.args.generation_max_length
										)
										snake_case:    List[str]      							=(
										    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:    Tuple      							=self.eval_dataset if eval_dataset is None else eval_dataset
										snake_case:    Any      							=self.get_eval_dataloader(lowerCamelCase__			)
										snake_case:    Optional[Any]      							=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:    int      							=self.compute_metrics
										snake_case:    int      							=None
										snake_case:    Union[str, Any]      							=time.time()
										snake_case:    int      							=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										try:
													snake_case:    Tuple      							=eval_loop(
													    lowerCamelCase__      ,						description='Evaluation'      ,						prediction_loss_only=True if compute_metrics is None else None      ,						ignore_keys=lowerCamelCase__      ,						metric_key_prefix=lowerCamelCase__      ,						)
										finally:
													snake_case:    Any      							=compute_metrics
										snake_case:    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(
										        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
													snake_case:    Optional[Any]      							=self.post_process_function(lowerCamelCase__      ,						lowerCamelCase__      ,						lowerCamelCase__			)
													snake_case:    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}_'''			):
																			snake_case:    Union[str, Any]      							=metrics.pop(lowerCamelCase__			)

													metrics.update(output.metrics			)
										else:
													snake_case:    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()			)

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




							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						Optional[Any]      ,						a_   :						Union[str, Any]      ,						a_   :						List[str]=None      ,						a_   :						str = "test"      ,						**a_   :						Optional[Any]			)			->					List[str]:
										snake_case:    Dict      							=gen_kwargs.copy()

										snake_case:    str      							=self.get_test_dataloader(lowerCamelCase__			)

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

										snake_case:    Optional[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 None or self.compute_metrics is None:
													return output

										snake_case:    str      							=self.post_process_function(lowerCamelCase__      ,						lowerCamelCase__      ,						lowerCamelCase__      ,						'predict'			)
										snake_case:    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}_'''			):
																snake_case:    Tuple      							=metrics.pop(lowerCamelCase__			)
										metrics.update(output.metrics			)
										return PredictionOutput(predictions=predictions.predictions      ,						label_ids=predictions.label_ids      ,						metrics=lowerCamelCase__			)












from __future__ import annotations


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  Optional[Any]:  # noqa: E741
   while r - l > 1:
      __UpperCamelCase	:			int					=					(l + r) // 2
      if v[m] >= key:
         __UpperCamelCase	:			Dict					=					m
      else:
         __UpperCamelCase	:			Optional[Any]					=					m  # noqa: E741
   return r


def 				__lowerCamelCase     (							__lowerCAmelCase							:					list[int]							)     ->  int:
   if len(__lowerCAmelCase							) == 0:
      return 0

   __UpperCamelCase	:			Optional[Any]					=					[0] * len(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					1

   __UpperCamelCase	:			Union[str, Any]					=					v[0]

   for i in range(1					,							len(__lowerCAmelCase							)							):
      if v[i] < tail[0]:
         __UpperCamelCase	:			Dict					=					v[i]
      elif v[i] > tail[length - 1]:
         __UpperCamelCase	:			Optional[int]					=					v[i]
         length += 1
      else:
         __UpperCamelCase	:			List[Any]					=					v[i]

   return length


if __name__ == "__main__":
       import doctest

       doctest.testmod()



