

"""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  (	lowercase   ):







 """simple docstring"""







 def __init__(			self							:			int,					*UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=None,					UpperCAmelCase__							:			Optional[Any]=None,					**UpperCAmelCase__							:			Optional[int]	):
    super().__init__(*UpperCAmelCase__,					**UpperCAmelCase__	)
    __lowercase								=						eval_examples
    __lowercase								=						post_process_function


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[Dataset] = None,					UpperCAmelCase__							:			Tuple=None,					UpperCAmelCase__							:			Optional[List[str]] = None,					UpperCAmelCase__							:			str = "eval",					**UpperCAmelCase__							:			List[str],					):
    __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(UpperCAmelCase__	)
    __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(
           UpperCAmelCase__,					description="Evaluation",					prediction_loss_only=True if compute_metrics is None else None,					ignore_keys=UpperCAmelCase__,					metric_key_prefix=UpperCAmelCase__,					)
    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(
            UpperCAmelCase__,					UpperCAmelCase__,					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(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
       __lowercase								=						self.compute_metrics(UpperCAmelCase__	)

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()	):
          if not key.startswith(F"""{metric_key_prefix}_"""	):
             __lowercase								=						metrics.pop(UpperCAmelCase__	)

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

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

    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,					UpperCAmelCase__	)
    return metrics



 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			str=None,					UpperCAmelCase__							:			str = "test",					**UpperCAmelCase__							:			str	):
    __lowercase								=						gen_kwargs.copy()

    __lowercase								=						self.get_test_dataloader(UpperCAmelCase__	)

    # 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(
           UpperCAmelCase__,					description="Prediction",					prediction_loss_only=True if compute_metrics is None else None,					ignore_keys=UpperCAmelCase__,					metric_key_prefix=UpperCAmelCase__,					)
    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(
            UpperCAmelCase__,					UpperCAmelCase__,					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(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					"predict"	)
    __lowercase								=						self.compute_metrics(UpperCAmelCase__	)

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







"""simple docstring"""





from __future__ import annotations

from typing import Any




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Any,					UpperCAmelCase__							:			int	):
    __lowercase								=						num_of_nodes
    __lowercase								=						[]
    __lowercase								=						{}


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):
    self.m_edges.append([u_node, v_node, weight]	)


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			int	):

    if self.m_component[u_node] == u_node:
       return u_node
    return self.find_component(self.m_component[u_node]	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			int	):

    if self.m_component[u_node] != u_node:
       for k in self.m_component:
          __lowercase								=						self.find_component(UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			list[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int	):

    if component_size[u_node] <= component_size[v_node]:
       __lowercase								=						v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(UpperCAmelCase__	)

    elif component_size[u_node] >= component_size[v_node]:
       __lowercase								=						self.find_component(UpperCAmelCase__	)
       component_size[u_node] += component_size[v_node]
       self.set_component(UpperCAmelCase__	)



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						[]
    __lowercase								=						0

    __lowercase								=						[-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes	):
       self.m_component.update({node: node}	)
       component_size.append(1	)

    __lowercase								=						self.m_num_of_nodes

    while num_of_components > 1:
       for edge in self.m_edges:
          __lowercase		,__lowercase		,__lowercase								=						edge

          __lowercase								=						self.m_component[u]
          __lowercase								=						self.m_component[v]

          if u_component != v_component:

             for component in (u_component, v_component):
                if (
                    minimum_weight_edge[component] == -1
                    or minimum_weight_edge[component][2] > w
                ):
                   __lowercase								=						[u, v, w]

       for edge in minimum_weight_edge:
          if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
             __lowercase		,__lowercase		,__lowercase								=						edge

             __lowercase								=						self.m_component[u]
             __lowercase								=						self.m_component[v]

             if u_component != v_component:
                mst_weight += w
                self.union(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
                print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
                num_of_components -= 1

       __lowercase								=						[-1] * self.m_num_of_nodes
    print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""	)







def   _A  (  )	->						None:
   '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()




