




from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowercase_			:       Optional[int]						       =				{
    '1': ['2', '3', '4', '5'],
    '2': ['1', '3', '4', '5'],
    '3': ['1', '2', '4', '5', '10'],
    '4': ['1', '2', '3', '5', '6'],
    '5': ['1', '2', '3', '4', '7'],
    '6': ['7', '8', '9', '10', '4'],
    '7': ['6', '8', '9', '10', '5'],
    '8': ['6', '7', '9', '10'],
    '9': ['6', '7', '8', '10'],
    '10': ['6', '7', '8', '9', '3'],
}




def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  {node: {node} for node in graph}

						_UpperCAmelCase									=  {node: graph[node][:] for node in graph}

						while len(snake_case_		) > 2:
												# Choose a random edge.
												_UpperCAmelCase									=  random.choice(list(graph_copy.keys()		)		)
												_UpperCAmelCase									=  random.choice(graph_copy[u]		)

												# Contract edge (u, v) to new node uv
												_UpperCAmelCase									=  u + v
												_UpperCAmelCase									=  list(set(graph_copy[u] + graph_copy[v]		)		)
												uv_neighbors.remove(snake_case_		)
												uv_neighbors.remove(snake_case_		)
												_UpperCAmelCase									=  uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(snake_case_		)

												_UpperCAmelCase									=  set(contracted_nodes[u].union(contracted_nodes[v]		)		)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(snake_case_		)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(snake_case_		)

    # Find cutset.
						_UpperCAmelCase									=  [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
				print(partition_graph(TEST_GRAPH))










import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
				import torch

				from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
				import pyanvml.pyanvml as nvml


lowercase_			:       int						       =				logging.get_logger(__name__)






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				PyTorchBenchmarkArguments
			snake_case_   :				PretrainedConfig
			snake_case_   :				str				  =  "PyTorch"







			@property
			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									return torch.__version__







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_inference   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_inference   )







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_train   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_train   )







			def UpperCamelCase    ( self					:    Dict					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									if self.args.torchscript:
															_UpperCAmelCase									=  True

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															except ImportError:
																					raise ImportError(
																					    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."   )
									else:
															_UpperCAmelCase									=  MODEL_MAPPING[config.__class__](snake_case__   )

									model.eval()
									model.to(self.args.device   )

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device   )

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision..."   )
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU."   )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					_UpperCAmelCase									=  torch.jit.trace(snake_case__					,					snake_case__   )
									else:
															_UpperCAmelCase									=  model

									def encoder_decoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__					,					decoder_input_ids=snake_case__   )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__   )
															return outputs

									_UpperCAmelCase									=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															except ImportError:
																					raise ImportError(
																					    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."   )
									else:
															_UpperCAmelCase									=  MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case__   )

									if self.args.torchscript:
															raise NotImplementedError("Training for torchscript is currently not implemented"   )
									else:
															_UpperCAmelCase									=  model

									model.train()
									model.to(self.args.device   )

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device   )

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision..."   )
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU."   )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															_UpperCAmelCase									=  train_model(snake_case__					,					labels=snake_case__   )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															_UpperCAmelCase									=  train_model(snake_case__					,					decoder_input_ids=snake_case__					,					labels=snake_case__   )[0]
															loss.backward()
															return loss

									_UpperCAmelCase									=  (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Tuple   ):




									"""simple docstring"""


									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"   )
																					timeit.repeat(
																					    snake_case__					,					repeat=1					,					number=5					,					)

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															_UpperCAmelCase									=  timeit.repeat(
															    snake_case__					,					repeat=self.args.repeat					,					number=10					,					)

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()   )

															return min(snake_case__   ) / 10.0
									except RuntimeError as e:
															self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
															return "N/A"







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    Callable[[], None]   ):




									"""simple docstring"""


									try:
															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  start_memory_tracing("transformers"   )

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
																					    " `--no-memory` or `args.memory=False`"   )
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    "py3nvml not installed, we won't log GPU memory usage. "
																											    "Install py3nvml (pip install py3nvml) to log information about GPU."   )
																											_UpperCAmelCase									=  "N/A"
																					else:
																											logger.info(
																											    "Measuring total GPU usage on GPU device. Make sure to not have additional processes running"
																											    " on the same GPU."   )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetMemoryInfo(snake_case__   )
																											_UpperCAmelCase									=  meminfo.used
																											_UpperCAmelCase									=  Memory(snake_case__   )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					_UpperCAmelCase									=  measure_peak_memory_cpu(snake_case__   )
																					_UpperCAmelCase									=  Memory(snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  stop_memory_tracing(snake_case__   )
															else:
																					_UpperCAmelCase									=  None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
															return "N/A", None




