'''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 		__A       (_UpperCAmelCase   ):






       def __init__(      self				,     *UpperCamelCase_				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              super().__init__(*lowercase__				,     **lowercase__      )
              __UpperCAmelCase  :				List[str]       				=		eval_examples
              __UpperCAmelCase  :				int       				=		post_process_function






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

              __UpperCAmelCase  :				Union[str, Any]       				=		self.eval_dataset if eval_dataset is None else eval_dataset
              __UpperCAmelCase  :				List[str]       				=		self.get_eval_dataloader(lowercase__      )
              __UpperCAmelCase  :				Optional[Any]       				=		self.eval_examples if eval_examples is None else eval_examples

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

                     # Prefix all keys with metric_key_prefix + '_'
                     for key in list(metrics.keys()      ):
                            if not key.startswith(f"""{metric_key_prefix}_"""      ):
                                   __UpperCAmelCase  :				Tuple       				=		metrics.pop(lowercase__      )

                     metrics.update(output.metrics      )
              else:
                     __UpperCAmelCase  :				Tuple       				=		output.metrics

              if self.args.should_log:
                     # Only the main node log the results by default
                     self.log(lowercase__      )

              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()      )

              __UpperCAmelCase  :				Optional[int]       				=		self.callback_handler.on_evaluate(self.args				,     self.state				,     self.control				,     lowercase__      )
              return metrics







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None				,     UpperCamelCase_ = "test"				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		gen_kwargs.copy()

              __UpperCAmelCase  :				List[str]       				=		self.get_test_dataloader(lowercase__      )

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

              __UpperCAmelCase  :				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(
                      lowercase__				,     lowercase__				,     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

              __UpperCAmelCase  :				List[Any]       				=		self.post_process_function(lowercase__				,     lowercase__				,     lowercase__				,     "predict"      )
              __UpperCAmelCase  :				Dict       				=		self.compute_metrics(lowercase__      )

              # Prefix all keys with metric_key_prefix + '_'
              for key in list(metrics.keys()      ):
                     if not key.startswith(f"""{metric_key_prefix}_"""      ):
                            __UpperCAmelCase  :				int       				=		metrics.pop(lowercase__      )
              metrics.update(output.metrics      )
              return PredictionOutput(predictions=predictions.predictions				,     label_ids=predictions.label_ids				,     metrics=lowercase__      )





'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              if src_parent == dst_parent:
                     return False

              if self.ranks[dst_parent] >= self.ranks[src_parent]:
                     self.set_counts[dst_parent] += self.set_counts[src_parent]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






