




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[int]			=						eval_examples
           _A							:     Tuple			=						post_process_function

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

           _A							:     Dict			=						self.eval_dataset if eval_dataset is None else eval_dataset
           _A							:     Optional[int]			=						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							:     List[str]			=						self.compute_metrics
           _A							:     Tuple			=						None
           _A							:     str			=						time.time()
           _A							:     Union[str, Any]			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                 _A							:     Optional[int]			=						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[str]			=						compute_metrics
           _A							:     Tuple			=						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							:     Tuple			=						self.post_process_function(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						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				)
           else:
                 _A							:     Union[str, 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							:     str			=						self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     _a				)
           return metrics



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

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

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

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







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(snake_case_     )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      _A							:     Any			=						36

      _A							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,snake_case_     )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()

