


'''simple docstring'''






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[Any]:
      for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['bs'] ,			model_result['ss']  ):
                _a      =							model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							'sgugger/tiny-distilbert-classification'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			only_pretrain_model=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			torchscript=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 @unittest.skipIf(torch_device == 'cpu' ,			'Cant do half precision'  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			fpaa=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      # set architectures equal to `None`
      _a      =							None
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			configs=[config]  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 @unittest.skipIf(torch_device == 'cpu' ,			'Can\'t do half precision'  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			fpaa=__magic_name__ ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			configs=[config]  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							'sshleifer/tinier_bart'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			configs=[config]  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			configs=[config]  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							'sshleifer/tinier_bart'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			configs=[config]  )
      _a      =							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							'sshleifer/tiny-gpt2'
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a      =							PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			save_to_csv=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			inference_time_csv_file=os.path.join(__magic_name__ ,			'inf_time.csv'  ) ,			train_memory_csv_file=os.path.join(__magic_name__ ,			'train_mem.csv'  ) ,			inference_memory_csv_file=os.path.join(__magic_name__ ,			'inf_mem.csv'  ) ,			train_time_csv_file=os.path.join(__magic_name__ ,			'train_time.csv'  ) ,			env_info_csv_file=os.path.join(__magic_name__ ,			'env.csv'  ) ,			multi_process=__magic_name__ ,			)
           _a      =							PyTorchBenchmark(__magic_name__  )
           benchmark.run()
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'inf_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'train_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'inf_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'train_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'env.csv'  )  ).exists()  )




 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							'sshleifer/tiny-gpt2'

      def _check_summary_is_not_empty(__magic_name__  ):
           self.assertTrue(hasattr(__magic_name__ ,			'sequential'  )  )
           self.assertTrue(hasattr(__magic_name__ ,			'cumulative'  )  )
           self.assertTrue(hasattr(__magic_name__ ,			'current'  )  )
           self.assertTrue(hasattr(__magic_name__ ,			'total'  )  )

      with tempfile.TemporaryDirectory() as tmp_dir:
           _a      =							PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			log_filename=os.path.join(__magic_name__ ,			'log.txt'  ) ,			log_print=__magic_name__ ,			trace_memory_line_by_line=__magic_name__ ,			multi_process=__magic_name__ ,			)
           _a      =							PyTorchBenchmark(__magic_name__  )
           _a      =							benchmark.run()
           _check_summary_is_not_empty(result.inference_summary  )
           _check_summary_is_not_empty(result.train_summary  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'log.txt'  )  ).exists()  )





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							0
 def __len__(	self  )   ->							int:
      return self.elements
 def __repr__(	self  )   ->							str:
      return str(self.heap  )
 def 							__UpperCAmelCase						(	self  )   ->							bool:
      # Check if the priority queue is empty
      return self.elements == 0
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
           self._swap_nodes(0 ,			self.elements - 1  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Swap the nodes at the given positions
      _a      =							self.heap[nodea_pos][0]
      _a      =							self.heap[nodea_pos][0]
      _a ,				_a      =							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      _a      =							nodea_pos
      _a      =							nodea_pos





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__magic_name__  )
      self.add_node(__magic_name__  )
      _a      =							weight
      _a      =							weight






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



     _a      =							{node: maxsize for node in graph.connections}
     _a      =							{node: None for node in graph.connections}

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

     # initialization
     _a      =							priority_queue.extract_min()
     _a      =							0
     for neighbour in graph.connections[node]:
          if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
               _a      =							dist[node] + graph.connections[node][neighbour]
               priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
               _a      =							node

    # running prim's algorithm
     while not priority_queue.is_empty():
          _a      =							priority_queue.extract_min()
          for neighbour in graph.connections[node]:
               if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                    _a      =							dist[node] + graph.connections[node][neighbour]
                    priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
                    _a      =							node
     return dist, parent

