


"""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__						(		_a    ):
      def __init__(		self	:	Optional[Any]      ,       *A_	:	str      ,       A_	:	List[str]=None      ,       A_	:	Dict=None      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


       super().__init__(*A_      ,       **A_							)
       __lowercase									=	eval_examples
       __lowercase									=	post_process_function
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Optional[Dataset] = None      ,       A_	:	Any=None      ,       A_	:	Optional[List[str]] = None      ,       A_	:	str = "eval"      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	gen_kwargs.copy()
       __lowercase									=	(
           gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""							) is not None else self.args.generation_max_length
       )
       __lowercase									=	(
           gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""							) is not None else self.args.generation_num_beams
       )
       __lowercase									=	gen_kwargs

       __lowercase									=	self.eval_dataset if eval_dataset is None else eval_dataset
       __lowercase									=	self.get_eval_dataloader(A_							)
       __lowercase									=	self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       __lowercase									=	self.compute_metrics
       __lowercase									=	None
       __lowercase									=	time.time()
       __lowercase									=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        __lowercase									=	eval_loop(
            A_      ,       description="""Evaluation"""      ,       prediction_loss_only=True if compute_metrics is None else None      ,       ignore_keys=A_      ,       metric_key_prefix=A_      ,       )
       finally:
        __lowercase									=	compute_metrics
       __lowercase									=	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
        __lowercase									=	self.post_process_function(A_      ,       A_      ,       A_							)
        __lowercase									=	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}_'''							):
          __lowercase									=	metrics.pop(A_							)

        metrics.update(output.metrics							)
       else:
        __lowercase									=	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()							)

       __lowercase									=	self.callback_handler.on_evaluate(self.args      ,       self.state      ,       self.control      ,       A_							)
       return metrics






      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Dict      ,       A_	:	Any      ,       A_	:	Tuple=None      ,       A_	:	str = "test"      ,       **A_	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	gen_kwargs.copy()

       __lowercase									=	self.get_test_dataloader(A_							)

       # Temporarily disable metric computation, we will do it in the loop here.
       __lowercase									=	self.compute_metrics
       __lowercase									=	None
       __lowercase									=	time.time()
       __lowercase									=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        __lowercase									=	eval_loop(
            A_      ,       description="""Prediction"""      ,       prediction_loss_only=True if compute_metrics is None else None      ,       ignore_keys=A_      ,       metric_key_prefix=A_      ,       )
       finally:
        __lowercase									=	compute_metrics

       __lowercase									=	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

       __lowercase									=	self.post_process_function(A_      ,       A_      ,       A_      ,       """predict"""							)
       __lowercase									=	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}_'''							):
         __lowercase									=	metrics.pop(A_							)
       metrics.update(output.metrics							)
       return PredictionOutput(predictions=predictions.predictions      ,       label_ids=predictions.label_ids      ,       metrics=A_							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

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

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

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


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

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

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 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()
