




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 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  *_a	,  _a=None	,  _a=None	,  **_a       )		->							Dict:
           super().__init__(*_a	,  **_a       )
           _A							:  Optional[Any]    =		eval_examples
           _A							:  int    =		post_process_function

     def       a__			(						self	,  _a = None	,  _a=None	,  _a = None	,  _a = "eval"	,  **_a	,  )		->							Dict[str, float]:
           _A							:  Tuple    =		gen_kwargs.copy()
           _A							:  int    =		(
               gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""       ) is not None else self.args.generation_max_length
           )
           _A							:  List[Any]    =		(
               gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.args.generation_num_beams
           )
           _A							:  str    =		gen_kwargs

           _A							:  Optional[Any]    =		self.eval_dataset if eval_dataset is None else eval_dataset
           _A							:  List[str]    =		self.get_eval_dataloader(_a       )
           _A							:  Any    =		self.eval_examples if eval_examples is None else eval_examples

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

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

           _A							:  Any    =		self.callback_handler.on_evaluate(self.args	,  self.state	,  self.control	,  _a       )
           return metrics



     def       a__			(						self	,  _a	,  _a	,  _a=None	,  _a = "test"	,  **_a       )		->							Union[str, Any]:
           _A							:  List[str]    =		gen_kwargs.copy()

           _A							:  Optional[int]    =		self.get_test_dataloader(_a       )

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

           _A							:  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(
                   _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

           _A							:  List[str]    =		self.post_process_function(_a	,  _a	,  _a	,  """predict"""       )
           _A							:  Union[str, 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}_'''       ):
                       _A							:  int    =		metrics.pop(_a       )
           metrics.update(output.metrics       )
           return PredictionOutput(predictions=predictions.predictions	,  label_ids=predictions.label_ids	,  metrics=_a       )







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

