






'''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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						str ,      lowercase							:						Optional[int]			)							->    Optional[Any]:

									'''simple docstring'''







									for model_result in results.values():
															for batch_size, sequence_length in zip(model_result["""bs"""] ,      model_result["""ss"""]			):
																					UpperCamelCase__         = model_result["""result"""][batch_size][sequence_length]
																					self.assertIsNotNone(lowercase			)






			def 				A      (   self							:						Optional[Any]			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						Any			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sgugger/tiny-distilbert-classification"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      only_pretrain_model=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      torchscript=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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 				A      (   self							:						int			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      fpaa=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						List[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									# set architectures equal to `None`
									UpperCamelCase__         = None
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      configs=[config]			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						Dict			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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 				A      (   self							:						Tuple			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      fpaa=lowercase ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result			)
									self.check_results_dict_not_empty(results.memory_train_result			)






			def 				A      (   self							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      configs=[config]			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						Optional[Any]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tinier_bart"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      configs=[config]			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result			)
									self.check_results_dict_not_empty(results.memory_inference_result			)






			def 				A      (   self							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      configs=[config]			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result			)
									self.check_results_dict_not_empty(results.memory_train_result			)






			def 				A      (   self							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tinier_bart"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      configs=[config]			)
									UpperCamelCase__         = benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result			)
									self.check_results_dict_not_empty(results.memory_train_result			)






			def 				A      (   self							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															UpperCamelCase__         = PyTorchBenchmarkArguments(
															    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      save_to_csv=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      inference_time_csv_file=os.path.join(lowercase ,      """inf_time.csv"""			) ,      train_memory_csv_file=os.path.join(lowercase ,      """train_mem.csv"""			) ,      inference_memory_csv_file=os.path.join(lowercase ,      """inf_mem.csv"""			) ,      train_time_csv_file=os.path.join(lowercase ,      """train_time.csv"""			) ,      env_info_csv_file=os.path.join(lowercase ,      """env.csv"""			) ,      multi_process=lowercase ,      )
															UpperCamelCase__         = PyTorchBenchmark(lowercase			)
															benchmark.run()
															self.assertTrue(Path(os.path.join(lowercase ,      """inf_time.csv"""			)			).exists()			)
															self.assertTrue(Path(os.path.join(lowercase ,      """train_time.csv"""			)			).exists()			)
															self.assertTrue(Path(os.path.join(lowercase ,      """inf_mem.csv"""			)			).exists()			)
															self.assertTrue(Path(os.path.join(lowercase ,      """train_mem.csv"""			)			).exists()			)
															self.assertTrue(Path(os.path.join(lowercase ,      """env.csv"""			)			).exists()			)







			def 				A      (   self							:						int			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""

									def _check_summary_is_not_empty(lowercase							:						List[str]			):
															self.assertTrue(hasattr(lowercase ,      """sequential"""			)			)
															self.assertTrue(hasattr(lowercase ,      """cumulative"""			)			)
															self.assertTrue(hasattr(lowercase ,      """current"""			)			)
															self.assertTrue(hasattr(lowercase ,      """total"""			)			)

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








'''simple docstring'''


from __future__ import annotations

from typing import Any
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any] ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = num_of_nodes
									UpperCamelCase__         = []
									UpperCamelCase__         = {}






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						int ,      lowercase							:						int ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									self.m_edges.append([u_node, v_node, weight]			)






			def 				A      (   self							:						str ,      lowercase							:						int			)							->    int:

									'''simple docstring'''







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






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







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






			def 				A      (   self							:						Tuple ,      lowercase							:						list[int] ,      lowercase							:						int ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									if component_size[u_node] <= component_size[v_node]:
															UpperCamelCase__         = v_node
															component_size[v_node] += component_size[u_node]
															self.set_component(lowercase			)

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







			def 				A      (   self							:						int			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = []
									UpperCamelCase__         = 0

									UpperCamelCase__         = [-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			)

									UpperCamelCase__         = self.m_num_of_nodes

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

																					UpperCamelCase__         = self.m_component[u]
																					UpperCamelCase__         = 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
																																	):
																																							UpperCamelCase__         = [u, v, w]

															for edge in minimum_weight_edge:
																					if isinstance(lowercase ,      lowercase			):
																											UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = edge

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

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

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







def 	__magic_name__( ):
						'''simple docstring'''







if __name__ == "__main__":
					import doctest

					doctest.testmod()
