


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_ (	a__   ):




      '''simple docstring'''


      def __init__(    self:	List[Any]						,				*a:	Tuple						,				a:	List[Any]=None						,				a:	Union[str, Any]=None						,				**a:	Tuple    ):
       super().__init__(*_lowerCamelCase						,				**_lowerCamelCase    )
       __lowerCamelCase		:							Dict		 =						eval_examples
       __lowerCamelCase		:							Optional[int]		 =						post_process_function




      def 					_snake_case     (    self:	Optional[int]						,				a:	Optional[Any] = None						,				a:	Any=None						,				a:	Union[str, Any] = None						,				a:	List[str] = "eval"						,				**a:	Optional[int]						,				):
       __lowerCamelCase		:							Any		 =						gen_kwargs.copy()
       __lowerCamelCase		:							str		 =						(
           gen_kwargs['''max_length'''] if gen_kwargs.get('max_length'    ) is not None else self.args.generation_max_length
       )
       __lowerCamelCase		:							Dict		 =						(
           gen_kwargs['''num_beams'''] if gen_kwargs.get('num_beams'    ) is not None else self.args.generation_num_beams
       )
       __lowerCamelCase		:							Any		 =						gen_kwargs

       __lowerCamelCase		:							List[Any]		 =						self.eval_dataset if eval_dataset is None else eval_dataset
       __lowerCamelCase		:							str		 =						self.get_eval_dataloader(_lowerCamelCase    )
       __lowerCamelCase		:							List[str]		 =						self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       __lowerCamelCase		:							Optional[Any]		 =						self.compute_metrics
       __lowerCamelCase		:							str		 =						None
       __lowerCamelCase		:							Optional[Any]		 =						time.time()
       __lowerCamelCase		:							List[str]		 =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        __lowerCamelCase		:							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:
        __lowerCamelCase		:							Dict		 =						compute_metrics
       __lowerCamelCase		:							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(
               _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
        __lowerCamelCase		:							Tuple		 =						self.post_process_function(_lowerCamelCase						,				_lowerCamelCase						,				_lowerCamelCase    )
        __lowerCamelCase		:							Optional[int]		 =						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}_'    ):
          __lowerCamelCase		:							Optional[int]		 =						metrics.pop(_lowerCamelCase    )

        metrics.update(output.metrics    )
       else:
        __lowerCamelCase		:							List[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()    )

       __lowerCamelCase		:							int		 =						self.callback_handler.on_evaluate(self.args						,				self.state						,				self.control						,				_lowerCamelCase    )
       return metrics




      def 					_snake_case     (    self:	str						,				a:	Optional[int]						,				a:	List[str]						,				a:	int=None						,				a:	Optional[Any] = "test"						,				**a:	Dict    ):
       __lowerCamelCase		:							str		 =						gen_kwargs.copy()

       __lowerCamelCase		:							str		 =						self.get_test_dataloader(_lowerCamelCase    )

       # Temporarily disable metric computation, we will do it in the loop here.
       __lowerCamelCase		:							Dict		 =						self.compute_metrics
       __lowerCamelCase		:							Tuple		 =						None
       __lowerCamelCase		:							List[str]		 =						time.time()
       __lowerCamelCase		:							Optional[Any]		 =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        __lowerCamelCase		:							Any		 =						eval_loop(
            _lowerCamelCase						,				description='Prediction'						,				prediction_loss_only=True if compute_metrics is None else None						,				ignore_keys=_lowerCamelCase						,				metric_key_prefix=_lowerCamelCase						,				)
       finally:
        __lowerCamelCase		:							Optional[int]		 =						compute_metrics

       __lowerCamelCase		:							List[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

       __lowerCamelCase		:							Tuple		 =						self.post_process_function(_lowerCamelCase						,				_lowerCamelCase						,				_lowerCamelCase						,				'predict'    )
       __lowerCamelCase		:							Dict		 =						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}_'    ):
         __lowerCamelCase		:							List[Any]		 =						metrics.pop(_lowerCamelCase    )
       metrics.update(output.metrics    )
       return PredictionOutput(predictions=predictions.predictions						,				label_ids=predictions.label_ids						,				metrics=_lowerCamelCase    )





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

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

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  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(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

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

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

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

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

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

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

