






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__     :   Accelerator	,snake_case__     :   int = 16	,snake_case__     :   str = "bert-base-cased"  ):




     '''simple docstring'''






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

     def tokenize_function(snake_case__     :   int  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[int]          =						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			:Tuple          =						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			:str          =						tokenized_datasets.rename_column("""label"""	,"""labels"""  )

     def collate_fn(snake_case__     :   str  ):
          # 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			:str          =						DataLoader(
         tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=snake_case__  )
     __snake_case			:Union[str, Any]          =						DataLoader(
         tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=snake_case__  )

     return train_dataloader, eval_dataloader






def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   Any  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						Accelerator()

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

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

     if accelerator.state.deepspeed_plugin is not None:
          __snake_case			:str          =						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			:Optional[Any]          =						get_linear_schedule_with_warmup(
              optimizer=snake_case__	,num_warmup_steps=0	,num_training_steps=snake_case__	,)
     else:
          __snake_case			:int          =						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			:Dict          =						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			:Optional[Any]          =						0
     # We also need to keep track of the stating epoch so files are named properly
     __snake_case			:Any          =						0

     # Now we train the model
     __snake_case			:Union[str, Any]          =						evaluate.load("""glue"""	,"""mrpc"""  )
     __snake_case			:str          =						0
     __snake_case			:List[Any]          =						{}
     for epoch in range(snake_case__	,snake_case__  ):
          model.train()
          for step, batch in enumerate(snake_case__  ):
               __snake_case			:Optional[int]          =						model(**snake_case__  )
               __snake_case			:List[Any]          =						outputs.loss
               __snake_case			:Any          =						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

          model.eval()
          __snake_case			:List[Any]          =						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			:str          =						model(**snake_case__  )
               __snake_case			:Any          =						outputs.logits.argmax(dim=-1  )
               # It is slightly faster to call this once, than multiple times
               __snake_case       , __snake_case			:str          =						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			:Optional[Any]          =						predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                         __snake_case			:Union[str, Any]          =						references[: len(eval_dataloader.dataset  ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=snake_case__	,references=snake_case__	,)

          __snake_case			:List[str]          =						metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''	,snake_case__  )
          __snake_case			:Union[str, Any]          =						eval_metric["""accuracy"""]

          if best_performance < eval_metric["accuracy"]:
               __snake_case			:Union[str, Any]          =						eval_metric["""accuracy"""]

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir	,"""all_results.json"""  )	,"""w"""  ) as f:
               json.dump(snake_case__	,snake_case__  )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						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(
         """--performance_lower_bound"""	,type=snake_case__	,default=snake_case__	,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""	,)
     parser.add_argument(
         """--num_epochs"""	,type=snake_case__	,default=3	,help="""Number of train epochs."""	,)
     __snake_case			:Optional[Any]          =						parser.parse_args()
     __snake_case			:Any          =						{"""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

import time

import numpy as np

lowerCamelCase__							=     [8, 5, 9, 7]
lowerCamelCase__							=     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowerCamelCase__							=     [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						) ->      None:






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return [
                sum(p_item[i] for p_item in self.__allocated_resources_table    )
                for i in range(len(self.__allocated_resources_table[0]    )    )
            ]



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return np.array(self.__claim_vector    ) - np.array(
                self.__processes_resource_summation()    )



       def 					__lowercase						(							self    ) ->      list[list[int]]:






            '''simple docstring'''





            return [
                list(np.array(self.__maximum_claim_table[i]    ) - np.array(a__    )    )
                for i, allocated_resource in enumerate(self.__allocated_resources_table    )
            ]



       def 					__lowercase						(							self    ) ->      dict[int, list[int]]:






            '''simple docstring'''





            return {self.__need().index(a__    ): i for i in self.__need()}



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.__need()
            __snake_case			:List[Any]          =						self.__allocated_resources_table
            __snake_case			:str          =						self.__available_resources()
            __snake_case			:List[Any]          =						self.__need_index_manager()
            for kw, val in kwargs.items():
                 if kw and val is True:
                      self.__pretty_data()
            print("""_""" * 50 + """\n"""    )
            while need_list:
                 __snake_case			:Dict          =						False
                 for each_need in need_list:
                      __snake_case			:Dict          =						True
                      for index, need in enumerate(a__    ):
                           if need > available_resources[index]:
                                __snake_case			:Dict          =						False
                                break
                      if execution:
                           __snake_case			:Any          =						True
                           # get the original index of the process from ind_ctrl db
                           for original_need_index, need_clone in need_index_manager.items():
                                if each_need == need_clone:
                                     __snake_case			:List[str]          =						original_need_index
                           print(F'''Process {process_number + 1} is executing.'''    )
                           # remove the process run from stack
                           need_list.remove(a__    )
                           # update available/freed resources stack
                           __snake_case			:Tuple          =						np.array(a__    ) + np.array(
                               alloc_resources_table[process_number]    )
                           print(
                               """Updated available resource stack for processes: """
                               + """ """.join([str(a__    ) for x in available_resources]    )    )
                           break
                 if safe:
                      print("""The process is in a safe state.\n"""    )
                 else:
                      print("""System in unsafe state. Aborting...\n"""    )
                      break



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            print(""" """ * 9 + """Allocated Resource Table"""    )
            for item in self.__allocated_resources_table:
                 print(
                     F'''P{self.__allocated_resources_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(""" """ * 9 + """System Resource Table"""    )
            for item in self.__maximum_claim_table:
                 print(
                     F'''P{self.__maximum_claim_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(
                """Current Usage by Active Processes: """
                + """ """.join(str(a__    ) for x in self.__claim_vector    )    )
            print(
                """Initial Available Resources:       """
                + """ """.join(str(a__    ) for x in self.__available_resources()    )    )
            time.sleep(1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


