






import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowerCamelCase__							=     logging.getLogger(__name__)




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				str 				= "sequence-classification"



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            if type(a__    ) == dict:
                 __snake_case			:List[Any]          =						Namespace(**a__    )
            __snake_case			:str          =						glue_output_modes[hparams.task]
            __snake_case			:Union[str, Any]          =						glue_tasks_num_labels[hparams.task]

            super().__init__(a__			,						a__			,						self.mode    )



       def 					__lowercase						(							self			,						**a__    ) ->      Optional[int]:






            '''simple docstring'''





            return self.model(**a__    )



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






            '''simple docstring'''





            __snake_case			:str          =						{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

            if self.config.model_type not in ["distilbert", "bart"]:
                 __snake_case			:Optional[Any]          =						batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

            __snake_case			:List[Any]          =						self(**a__    )
            __snake_case			:List[Any]          =						outputs[0]

            __snake_case			:Any          =						self.trainer.lr_schedulers[0]["""scheduler"""]
            __snake_case			:List[str]          =						{"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
            return {"loss": loss, "log": tensorboard_logs}



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.hparams
            __snake_case			:int          =						processors[args.task]()
            __snake_case			:Optional[Any]          =						processor.get_labels()

            for mode in ["train", "dev"]:
                 __snake_case			:str          =						self._feature_file(a__    )
                 if os.path.exists(a__    ) and not args.overwrite_cache:
                      logger.info("""Loading features from cached file %s"""			,						a__    )
                 else:
                      logger.info("""Creating features from dataset file at %s"""			,						args.data_dir    )
                      __snake_case			:Dict          =						(
                          processor.get_dev_examples(args.data_dir    )
                          if mode == """dev"""
                          else processor.get_train_examples(args.data_dir    )
                      )
                      __snake_case			:int          =						convert_examples_to_features(
                          a__			,						self.tokenizer			,						max_length=args.max_seq_length			,						label_list=self.labels			,						output_mode=args.glue_output_mode			,						)
                      logger.info("""Saving features into cached file %s"""			,						a__    )
                      torch.save(a__			,						a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = False    ) ->      DataLoader:






            '''simple docstring'''





            __snake_case			:str          =						"""dev""" if mode == """test""" else mode

            __snake_case			:List[Any]          =						self._feature_file(a__    )
            logger.info("""Loading features from cached file %s"""			,						a__    )
            __snake_case			:Union[str, Any]          =						torch.load(a__    )
            __snake_case			:str          =						torch.tensor([f.input_ids for f in features]			,						dtype=torch.long    )
            __snake_case			:List[str]          =						torch.tensor([f.attention_mask for f in features]			,						dtype=torch.long    )
            __snake_case			:Optional[Any]          =						torch.tensor([f.token_type_ids for f in features]			,						dtype=torch.long    )
            if self.hparams.glue_output_mode == "classification":
                 __snake_case			:List[Any]          =						torch.tensor([f.label for f in features]			,						dtype=torch.long    )
            elif self.hparams.glue_output_mode == "regression":
                 __snake_case			:str          =						torch.tensor([f.label for f in features]			,						dtype=torch.float    )

            return DataLoader(
                TensorDataset(a__			,						a__			,						a__			,						a__    )			,						batch_size=a__			,						shuffle=a__			,						)



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:List[str]          =						{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

            if self.config.model_type not in ["distilbert", "bart"]:
                 __snake_case			:int          =						batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

            __snake_case			:Union[str, Any]          =						self(**a__    )
            __snake_case       , __snake_case			:str          =						outputs[:2]
            __snake_case			:Optional[Any]          =						logits.detach().cpu().numpy()
            __snake_case			:List[Any]          =						inputs["""labels"""].detach().cpu().numpy()

            return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



       def 					__lowercase						(							self			,						a__    ) ->      tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						torch.stack([x["""val_loss"""] for x in outputs]    ).mean().detach().cpu().item()
            __snake_case			:Union[str, Any]          =						np.concatenate([x["""pred"""] for x in outputs]			,						axis=0    )

            if self.hparams.glue_output_mode == "classification":
                 __snake_case			:Dict          =						np.argmax(a__			,						axis=1    )
            elif self.hparams.glue_output_mode == "regression":
                 __snake_case			:Dict          =						np.squeeze(a__    )

            __snake_case			:List[Any]          =						np.concatenate([x["""target"""] for x in outputs]			,						axis=0    )
            __snake_case			:Union[str, Any]          =						[[] for _ in range(out_label_ids.shape[0]    )]
            __snake_case			:Optional[int]          =						[[] for _ in range(out_label_ids.shape[0]    )]

            __snake_case			:Dict          =						{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task			,						a__			,						a__    )}

            __snake_case			:Union[str, Any]          =						dict(results.items()    )
            __snake_case			:Optional[Any]          =						results
            return ret, preds_list, out_label_list



       def 					__lowercase						(							self			,						a__    ) ->      dict:






            '''simple docstring'''





            __snake_case       , __snake_case       , __snake_case			:Optional[int]          =						self._eval_end(a__    )
            __snake_case			:List[str]          =						ret["""log"""]
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



       def 					__lowercase						(							self			,						a__    ) ->      dict:






            '''simple docstring'''





            __snake_case       , __snake_case       , __snake_case			:Optional[Any]          =						self._eval_end(a__    )
            __snake_case			:Union[str, Any]          =						ret["""log"""]
            # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
            return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



       @staticmethod
       def 					__lowercase						(							a__			,						a__    ) ->      Optional[Any]:






            '''simple docstring'''





            BaseTransformer.add_model_specific_args(a__			,						a__    )
            parser.add_argument(
                """--max_seq_length"""			,						default=1_28			,						type=a__			,						help=(
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )			,						)

            parser.add_argument(
                """--task"""			,						default=""""""			,						type=a__			,						required=a__			,						help="""The GLUE task to run"""			,						)
            parser.add_argument(
                """--gpus"""			,						default=0			,						type=a__			,						help="""The number of GPUs allocated for this, it is by default 0 meaning none"""			,						)

            parser.add_argument(
                """--overwrite_cache"""			,						action="""store_true"""			,						help="""Overwrite the cached training and evaluation sets"""    )

            return parser






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Any          =						argparse.ArgumentParser()
     add_generic_args(snake_case__	,os.getcwd()  )
     __snake_case			:List[str]          =						GLUETransformer.add_model_specific_args(snake_case__	,os.getcwd()  )
     __snake_case			:Any          =						parser.parse_args()

     # If output_dir not provided, a folder will be generated in pwd
     if args.output_dir is None:
          __snake_case			:List[str]          =						os.path.join(
              """./results"""	,f'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}'''	,)
          os.makedirs(args.output_dir  )

     __snake_case			:int          =						GLUETransformer(snake_case__  )
     __snake_case			:Optional[Any]          =						generic_train(snake_case__	,snake_case__  )

     # Optionally, predict on dev set and write to output_dir
     if args.do_predict:
          __snake_case			:Any          =						sorted(glob.glob(os.path.join(args.output_dir	,"""checkpoint-epoch=*.ckpt"""  )	,recursive=snake_case__  )  )
          __snake_case			:List[str]          =						model.load_from_checkpoint(checkpoints[-1]  )
          return trainer.test(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()


