





'''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    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     *__lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       super().__init__(*__lowerCAmelCase			,     **__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							eval_examples
       UpperCamelCase_				:   str					    =							post_process_function
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase = None			,     __lowerCAmelCase=None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "eval"			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   Dict					    =							gen_kwargs.copy()
       UpperCamelCase_				:   List[Any]					    =							(
           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_				:   Tuple					    =							gen_kwargs

       UpperCamelCase_				:   Union[str, Any]					    =							self.eval_dataset if eval_dataset is None else eval_dataset
       UpperCamelCase_				:   Optional[Any]					    =							self.get_eval_dataloader(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							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[int]					    =							None
       UpperCamelCase_				:   Union[str, Any]					    =							time.time()
       UpperCamelCase_				:   str					    =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             UpperCamelCase_				:   Any					    =							eval_loop(
                 __lowerCAmelCase			,     description="""Evaluation"""			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCAmelCase			,     metric_key_prefix=__lowerCAmelCase			,     )
       finally:
             UpperCamelCase_				:   Any					    =							compute_metrics
       UpperCamelCase_				:   Optional[int]					    =							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
             UpperCamelCase_				:   Any					    =							self.post_process_function(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   List[Any]					    =							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}_" ):
                         UpperCamelCase_				:   Dict					    =							metrics.pop(__lowerCAmelCase )

             metrics.update(output.metrics )
       else:
             UpperCamelCase_				:   str					    =							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() )

       UpperCamelCase_				:   int					    =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCAmelCase )
       return metrics





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase = "test"			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							gen_kwargs.copy()

       UpperCamelCase_				:   Optional[int]					    =							self.get_test_dataloader(__lowerCAmelCase )

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

       UpperCamelCase_				:   Optional[int]					    =							self.post_process_function(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     """predict""" )
       UpperCamelCase_				:   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}_" ):
                   UpperCamelCase_				:   int					    =							metrics.pop(__lowerCAmelCase )
       metrics.update(output.metrics )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCAmelCase )














'''simple docstring'''

import random



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: bool = False	)       ->				dict:





      """simple docstring"""

      UpperCamelCase_				:   dict					    =							{i: [] for i in range(a_	)}

      # if probability is greater or equal than 1, then generate a complete graph
      if probability >= 1:
            return complete_graph(a_	)
      # if probability is lower or equal than 0, then return a graph without edges
      if probability <= 0:
            return graph

      # for each couple of nodes, add an edge from u to v
      # if the number randomly generated is greater than probability probability
      for i in range(a_	):
            for j in range(i + 1    ,   a_	):
                  if random.random() < probability:
                        graph[i].append(a_	)
                        if not directed:
                              # if the graph is undirected, add an edge in from j to i, either
                              graph[j].append(a_	)
      return graph



def 							snake_case					(     a_							: int	)       ->				dict:





      """simple docstring"""

      return {
          i: [j for j in range(a_	) if i != j] for i in range(a_	)
      }


if __name__ == "__main__":
      import doctest

      doctest.testmod()






