






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__							=     16
lowerCamelCase__							=     32






def   UpperCamelCase  (       snake_case_     :   Optional[int]	,snake_case_     :   List[str] = 16	,snake_case_     :   List[str] = "bert-base-cased"  ):




     '''simple docstring'''






     __snake_case			:Dict          =						AutoTokenizer.from_pretrained(snake_case_  )
     __snake_case			:List[Any]          =						load_dataset("""glue"""	,"""mrpc"""  )

     def tokenize_function(snake_case_     :   Dict  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:str          =						tokenizer(examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case_	,max_length=snake_case_  )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:Dict          =						datasets.map(
         snake_case_	,batched=snake_case_	,remove_columns=["""idx""", """sentence1""", """sentence2"""]	,load_from_cache_file=snake_case_  )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __snake_case			:Tuple          =						tokenized_datasets.rename_column("""label"""	,"""labels"""  )

     def collate_fn(snake_case_     :   Dict  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case_	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case_	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:int          =						DataLoader(
         tokenized_datasets["""train"""]	,shuffle=snake_case_	,collate_fn=snake_case_	,batch_size=snake_case_  )
     __snake_case			:Dict          =						DataLoader(
         tokenized_datasets["""validation"""]	,shuffle=snake_case_	,collate_fn=snake_case_	,batch_size=snake_case_  )

     return train_dataloader, eval_dataloader






def   UpperCamelCase  (       snake_case_     :   int	,snake_case_     :   Optional[int]	,snake_case_     :   List[Any]	,snake_case_     :   int  ):




     '''simple docstring'''






     model.eval()
     __snake_case			:Tuple          =						0
     for step, batch in enumerate(snake_case_  ):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device  )
          with torch.no_grad():
               __snake_case			:Optional[int]          =						model(**snake_case_  )
          __snake_case			:Optional[int]          =						outputs.logits.argmax(dim=-1  )
          # It is slightly faster to call this once, than multiple times
          __snake_case       , __snake_case			:List[Any]          =						accelerator.gather(
              (predictions, batch["""labels"""])  )  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(snake_case_  ) - 1:
                    __snake_case			:Dict          =						predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                    __snake_case			:Tuple          =						references[: len(eval_dataloader.dataset  ) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=snake_case_	,references=snake_case_	,)

     __snake_case			:Optional[int]          =						metric.compute()
     return eval_metric["accuracy"]






def   UpperCamelCase  (       snake_case_     :   Optional[Any]	,snake_case_     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __snake_case			:List[Any]          =						config["""lr"""]
     __snake_case			:Tuple          =						int(config["""num_epochs"""]  )
     __snake_case			:List[Any]          =						int(config["""seed"""]  )
     __snake_case			:Dict          =						int(config["""batch_size"""]  )
     __snake_case			:Union[str, Any]          =						args.model_name_or_path

     set_seed(snake_case_  )
     __snake_case       , __snake_case			:Tuple          =						get_dataloaders(snake_case_	,snake_case_	,snake_case_  )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __snake_case			:Dict          =						AutoModelForSequenceClassification.from_pretrained(snake_case_	,return_dict=snake_case_  )

     # Instantiate optimizer
     __snake_case			:str          =						(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     __snake_case			:List[Any]          =						optimizer_cls(params=model.parameters()	,lr=snake_case_  )

     if accelerator.state.deepspeed_plugin is not None:
          __snake_case			:Dict          =						accelerator.state.deepspeed_plugin.deepspeed_config[
              """gradient_accumulation_steps"""
          ]
     else:
          __snake_case			:Dict          =						1
     __snake_case			:List[str]          =						(len(snake_case_  ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __snake_case			:Any          =						get_linear_schedule_with_warmup(
              optimizer=snake_case_	,num_warmup_steps=0	,num_training_steps=snake_case_	,)
     else:
          __snake_case			:List[Any]          =						DummyScheduler(snake_case_	,total_num_steps=snake_case_	,warmup_num_steps=0  )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __snake_case       , __snake_case       , __snake_case       , __snake_case       , __snake_case			:List[Any]          =						accelerator.prepare(
         snake_case_	,snake_case_	,snake_case_	,snake_case_	,snake_case_  )

     # We need to keep track of how many total steps we have iterated over
     __snake_case			:Union[str, Any]          =						0
     # We also need to keep track of the stating epoch so files are named properly
     __snake_case			:Any          =						0
     __snake_case			:Tuple          =						evaluate.load("""glue"""	,"""mrpc"""  )
     __snake_case			:Tuple          =						num_epochs

     if args.partial_train_epoch is not None:
          __snake_case			:Any          =						args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint  )
          __snake_case			:Dict          =						args.resume_from_checkpoint.split("""epoch_"""  )[1]
          __snake_case			:Tuple          =						""""""
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          __snake_case			:Any          =						int(snake_case_  ) + 1
          __snake_case			:Union[str, Any]          =						evaluation_loop(snake_case_	,snake_case_	,snake_case_	,snake_case_  )
          accelerator.print("""resumed checkpoint performance:"""	,snake_case_  )
          accelerator.print("""resumed checkpoint's scheduler's lr:"""	,lr_scheduler.get_lr()[0]  )
          accelerator.print("""resumed optimizers's lr:"""	,optimizer.param_groups[0]["""lr"""]  )
          with open(os.path.join(args.output_dir	,f'''state_{starting_epoch-1}.json'''  )	,"""r"""  ) as f:
               __snake_case			:Optional[int]          =						json.load(snake_case_  )
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     __snake_case			:List[str]          =						{}
     for epoch in range(snake_case_	,snake_case_  ):
          model.train()
          for step, batch in enumerate(snake_case_  ):
               __snake_case			:Union[str, Any]          =						model(**snake_case_  )
               __snake_case			:int          =						outputs.loss
               __snake_case			:Tuple          =						loss / gradient_accumulation_steps
               accelerator.backward(snake_case_  )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          __snake_case			:List[str]          =						f'''epoch_{epoch}'''
          __snake_case			:Dict          =						os.path.join(args.output_dir	,snake_case_  )
          accelerator.save_state(snake_case_  )
          __snake_case			:Optional[int]          =						evaluation_loop(snake_case_	,snake_case_	,snake_case_	,snake_case_  )
          __snake_case			:Union[str, Any]          =						accuracy
          __snake_case			:Union[str, Any]          =						lr_scheduler.get_lr()[0]
          __snake_case			:Tuple          =						optimizer.param_groups[0]["""lr"""]
          __snake_case			:Optional[Any]          =						epoch
          __snake_case			:str          =						overall_step
          accelerator.print(f'''epoch {epoch}:'''	,snake_case_  )

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir	,f'''state_{epoch}.json'''  )	,"""w"""  ) as f:
                    json.dump(snake_case_	,snake_case_  )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:str          =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""  )
     parser.add_argument(
         """--model_name_or_path"""	,type=snake_case_	,default="""bert-base-cased"""	,help="""Path to pretrained model or model identifier from huggingface.co/models."""	,required=snake_case_	,)
     parser.add_argument(
         """--output_dir"""	,type=snake_case_	,default="""."""	,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,)
     parser.add_argument(
         """--resume_from_checkpoint"""	,type=snake_case_	,default=snake_case_	,help="""If the training should continue from a checkpoint folder."""	,)
     parser.add_argument(
         """--partial_train_epoch"""	,type=snake_case_	,default=snake_case_	,help="""If passed, the training will stop after this number of epochs."""	,)
     parser.add_argument(
         """--num_epochs"""	,type=snake_case_	,default=2	,help="""Number of train epochs."""	,)
     __snake_case			:Tuple          =						parser.parse_args()
     __snake_case			:List[str]          =						{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

     training_function(snake_case_	,snake_case_  )


if __name__ == "__main__":
     main()










from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:list[list[Edge]]          =						[[] for _ in range(a__    )]
            __snake_case			:List[str]          =						size



       def __getitem__(							self			,						a__    ) ->      Iterator[Edge]:






            '''simple docstring'''





            return iter(self._graph[vertex]    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._size



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            if weight not in (0, 1):
                 raise ValueError("""Edge weight must be either 0 or 1."""    )

            if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("""Vertex indexes must be in [0; size)."""    )

            self._graph[from_vertex].append(Edge(a__			,						a__    )    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int | None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						deque([start_vertex]    )
            __snake_case			:list[int | None]          =						[None] * self.size
            __snake_case			:Tuple          =						0

            while queue:
                 __snake_case			:List[Any]          =						queue.popleft()
                 __snake_case			:Optional[Any]          =						distances[current_vertex]
                 if current_distance is None:
                      continue

                 for edge in self[current_vertex]:
                      __snake_case			:Optional[Any]          =						current_distance + edge.weight
                      __snake_case			:Dict          =						distances[edge.destination_vertex]
                      if (
                          isinstance(a__			,						a__    )
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __snake_case			:Dict          =						new_distance
                      if edge.weight == 0:
                           queue.appendleft(edge.destination_vertex    )
                      else:
                           queue.append(edge.destination_vertex    )

            if distances[finish_vertex] is None:
                 raise ValueError("""No path from start_vertex to finish_vertex."""    )

            return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()


