




import math
import time

from transformers import Trainer, 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      (      __snake_case					):
							def __init__(		self		:Dict						, *__magic_name__		:Dict						, __magic_name__		:Union[str, Any]=None						, __magic_name__		:str=None						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								super().__init__(*__lowerCamelCase						, **__lowerCamelCase							)
								a									=				eval_examples
								a									=				post_process_function
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=None						, __magic_name__		:int=None						, __magic_name__		:Tuple=None						, __magic_name__		:str = "eval"							):







								'''simple docstring'''







								a									=				self.eval_dataset if eval_dataset is None else eval_dataset
								a									=				self.get_eval_dataloader(__lowerCamelCase							)
								a									=				self.eval_examples if eval_examples is None else eval_examples

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

								a									=				self.callback_handler.on_evaluate(self.args						, self.state						, self.control						, __lowerCamelCase							)
								return metrics





							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Union[str, Any]						, __magic_name__		:Any						, __magic_name__		:Dict=None						, __magic_name__		:str = "test"							):







								'''simple docstring'''







								a									=				self.get_test_dataloader(__lowerCamelCase							)

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

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





def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ )

   for i in range(n - 1 ):
      for j in range(i + 1	,    lowerCAmelCase_ ):
         if arr[i] > arr[j]:
            num_inversions += 1

   return num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   if len(lowerCAmelCase_ ) <= 1:
      return arr, 0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) // 2
   UpperCAmelCase      					=  arr[0:mid]
   UpperCAmelCase      					=  arr[mid:]

   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  _count_cross_inversions(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  inversion_p + inversions_q + cross_inversions
   return c, num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  UpperCAmelCase      					=  UpperCAmelCase      					=  0
   while i < len(lowerCAmelCase_ ) and j < len(lowerCAmelCase_ ):
      if p[i] > q[j]:
         # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
         # These are all inversions. The claim emerges from the
         # property that P is sorted.
         num_inversion += len(lowerCAmelCase_ ) - i
         r.append(q[j] )
         j += 1
      else:
         r.append(p[i] )
         i += 1

   if i < len(lowerCAmelCase_ ):
      r.extend(p[i:] )
   else:
      r.extend(q[j:] )

   return r, num_inversion
def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  [1_0, 2, 1, 5, 5, 2, 1_1]

   # this arr has 8 inversions:
   # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 8

   print("""number of inversions = """	,    lowerCAmelCase_ )

   # testing an array with zero inversion (a sorted arr_1)

   arr_a.sort()
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )

   # an empty list should also have zero inversions
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )


if __name__ == "__main__":
 main()



