from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__UpperCamelCase				:  Optional[Any]      					=							'Create a default config file for Accelerate with only a few flags set.'
def      snake_case_						( __lowercase="no"			,       __lowercase = default_json_config_file			,       __lowercase = False       ):
   UpperCAmelCase_							:      Tuple						=   Path(_snake_case       )
   path.parent.mkdir(parents=_snake_case			,       exist_ok=_snake_case       )
   if path.exists():
      print(
          F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''       )
      return False
   UpperCAmelCase_							:      List[str]						=   mixed_precision.lower()
   if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
      raise ValueError(
          F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''       )
   UpperCAmelCase_							:      Tuple						=   {
       '''compute_environment''': '''LOCAL_MACHINE''',
       '''mixed_precision''': mixed_precision,
   }
   if torch.cuda.is_available():
      UpperCAmelCase_							:      Union[str, Any]						=   torch.cuda.device_count()
      UpperCAmelCase_							:      Tuple						=   num_gpus
      UpperCAmelCase_							:      int						=   False
      if num_gpus > 1:
         UpperCAmelCase_							:      List[str]						=   '''MULTI_GPU'''
      else:
         UpperCAmelCase_							:      str						=   '''NO'''
   elif is_xpu_available() and use_xpu:
      UpperCAmelCase_							:      Optional[Any]						=   torch.xpu.device_count()
      UpperCAmelCase_							:      List[Any]						=   num_xpus
      UpperCAmelCase_							:      int						=   False
      if num_xpus > 1:
         UpperCAmelCase_							:      Any						=   '''MULTI_XPU'''
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   '''NO'''
   elif is_npu_available():
      UpperCAmelCase_							:      Union[str, Any]						=   torch.npu.device_count()
      UpperCAmelCase_							:      Dict						=   num_npus
      UpperCAmelCase_							:      int						=   False
      if num_npus > 1:
         UpperCAmelCase_							:      List[Any]						=   '''MULTI_NPU'''
      else:
         UpperCAmelCase_							:      Dict						=   '''NO'''
   else:
      UpperCAmelCase_							:      Any						=   0
      UpperCAmelCase_							:      int						=   True
      UpperCAmelCase_							:      str						=   1
      UpperCAmelCase_							:      List[str]						=   '''NO'''
   UpperCAmelCase_							:      Optional[int]						=   ClusterConfig(**_snake_case       )
   config.to_json_file(_snake_case       )
   return path
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      str						=   parser.add_parser('''default'''			,       parents=_snake_case			,       help=_snake_case			,       formatter_class=_snake_case       )
   parser.add_argument(
       '''--config_file'''			,       default=_snake_case			,       help=(
           '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
           '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
           '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
           '''with \'huggingface\'.'''
       )			,       dest='''save_location'''			,       )

   parser.add_argument(
       '''--mixed_precision'''			,       choices=['''no''', '''fp16''', '''bf16''']			,       type=_snake_case			,       help='''Whether or not to use mixed precision training. '''
       '''Choose between FP16 and BF16 (bfloat16) training. '''
       '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''			,       default='''no'''			,       )
   parser.set_defaults(func=_snake_case       )
   return parser


def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   write_basic_config(args.mixed_precision			,       args.save_location       )
   if config_file:
      print(F'''accelerate configuration saved at {config_file}'''       )



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__UpperCamelCase				:  Dict      					=							logging.getLogger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Optional[int]			= 'token-classification'





    def __init__(	self				:   Any       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       if type(__snake_case	) == dict:
          UpperCAmelCase_							:      Tuple						=   Namespace(**__snake_case	)
       UpperCAmelCase_							:      Dict						=   import_module('''tasks'''	)
       try:
          UpperCAmelCase_							:      int						=   getattr(__snake_case       ,   hparams.task_type	)
          UpperCAmelCase_							:      TokenClassificationTask						=   token_classification_task_clazz()
       except AttributeError:
          raise ValueError(
              f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
              f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''	)
       UpperCAmelCase_							:      int						=   self.token_classification_task.get_labels(hparams.labels	)
       UpperCAmelCase_							:      Dict						=   CrossEntropyLoss().ignore_index
       super().__init__(__snake_case       ,   len(self.labels	)       ,   self.mode	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   **__snake_case				:   Optional[Any]	):





       '''simple docstring'''
       return self.model(**__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
       if self.config.model_type != "distilbert":
          UpperCAmelCase_							:      Union[str, Any]						=   (
              batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
          )  # XLM and RoBERTa don"t use token_type_ids

       UpperCAmelCase_							:      str						=   self(**__snake_case	)
       UpperCAmelCase_							:      Any						=   outputs[0]
       # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss}





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.hparams
       for mode in ["train", "dev", "test"]:
          UpperCAmelCase_							:      Optional[Any]						=   self._feature_file(__snake_case	)
          if os.path.exists(__snake_case	) and not args.overwrite_cache:
             logger.info('''Loading features from cached file %s'''       ,   __snake_case	)
             UpperCAmelCase_							:      Any						=   torch.load(__snake_case	)
          else:
             logger.info('''Creating features from dataset file at %s'''       ,   args.data_dir	)
             UpperCAmelCase_							:      Optional[Any]						=   self.token_classification_task.read_examples_from_file(args.data_dir       ,   __snake_case	)
             UpperCAmelCase_							:      List[str]						=   self.token_classification_task.convert_examples_to_features(
                 __snake_case       ,   self.labels       ,   args.max_seq_length       ,   self.tokenizer       ,   cls_token_at_end=bool(self.config.model_type in ['''xlnet''']	)       ,   cls_token=self.tokenizer.cls_token       ,   cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0       ,   sep_token=self.tokenizer.sep_token       ,   sep_token_extra=__snake_case       ,   pad_on_left=bool(self.config.model_type in ['''xlnet''']	)       ,   pad_token=self.tokenizer.pad_token_id       ,   pad_token_segment_id=self.tokenizer.pad_token_type_id       ,   pad_token_label_id=self.pad_token_label_id       ,   )
             logger.info('''Saving features into cached file %s'''       ,   __snake_case	)
             torch.save(__snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   int       ,   __snake_case				:   bool = False	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self._feature_file(__snake_case	)
       logger.info('''Loading features from cached file %s'''       ,   __snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   torch.load(__snake_case	)
       UpperCAmelCase_							:      Any						=   torch.tensor([f.input_ids for f in features]       ,   dtype=torch.long	)
       UpperCAmelCase_							:      int						=   torch.tensor([f.attention_mask for f in features]       ,   dtype=torch.long	)
       if features[0].token_type_ids is not None:
          UpperCAmelCase_							:      Any						=   torch.tensor([f.token_type_ids for f in features]       ,   dtype=torch.long	)
       else:
          UpperCAmelCase_							:      Optional[int]						=   torch.tensor([0 for f in features]       ,   dtype=torch.long	)
          # HACK(we will not use this anymore soon)
       UpperCAmelCase_							:      int						=   torch.tensor([f.label_ids for f in features]       ,   dtype=torch.long	)
       return DataLoader(
           TensorDataset(__snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case	)       ,   batch_size=__snake_case	)





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       """Compute validation""" ""
       UpperCAmelCase_							:      str						=   {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
       if self.config.model_type != "distilbert":
          UpperCAmelCase_							:      Any						=   (
              batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
          )  # XLM and RoBERTa don"t use token_type_ids
       UpperCAmelCase_							:      int						=   self(**__snake_case	)
       UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   outputs[:2]
       UpperCAmelCase_							:      Optional[int]						=   logits.detach().cpu().numpy()
       UpperCAmelCase_							:      List[Any]						=   inputs['''labels'''].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   torch.stack([x['''val_loss'''] for x in outputs]	).mean()
       UpperCAmelCase_							:      Dict						=   np.concatenate([x['''pred'''] for x in outputs]       ,   axis=0	)
       UpperCAmelCase_							:      Any						=   np.argmax(__snake_case       ,   axis=2	)
       UpperCAmelCase_							:      int						=   np.concatenate([x['''target'''] for x in outputs]       ,   axis=0	)

       UpperCAmelCase_							:      Tuple						=   dict(enumerate(self.labels	)	)
       UpperCAmelCase_							:      Union[str, Any]						=   [[] for _ in range(out_label_ids.shape[0]	)]
       UpperCAmelCase_							:      Optional[int]						=   [[] for _ in range(out_label_ids.shape[0]	)]

       for i in range(out_label_ids.shape[0]	):
          for j in range(out_label_ids.shape[1]	):
             if out_label_ids[i, j] != self.pad_token_label_id:
                out_label_list[i].append(label_map[out_label_ids[i][j]]	)
                preds_list[i].append(label_map[preds[i][j]]	)

       UpperCAmelCase_							:      Union[str, Any]						=   {
           '''val_loss''': val_loss_mean,
           '''accuracy_score''': accuracy_score(__snake_case       ,   __snake_case	),
           '''precision''': precision_score(__snake_case       ,   __snake_case	),
           '''recall''': recall_score(__snake_case       ,   __snake_case	),
           '''f1''': fa_score(__snake_case       ,   __snake_case	),
       }

       UpperCAmelCase_							:      str						=   dict(results.items()	)
       UpperCAmelCase_							:      List[Any]						=   results
       return ret, preds_list, out_label_list





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   int	):





       '''simple docstring'''
       # when stable
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   self._eval_end(__snake_case	)
       UpperCAmelCase_							:      int						=   ret['''log''']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       # updating to test_epoch_end instead of deprecated test_end
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   self._eval_end(__snake_case	)

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       UpperCAmelCase_							:      Optional[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 					_lowerCamelCase (	__snake_case				:   str       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       # Add NER specific options
       BaseTransformer.add_model_specific_args(__snake_case       ,   __snake_case	)
       parser.add_argument(
           '''--task_type'''       ,   default='''NER'''       ,   type=__snake_case       ,   help='''Task type to fine tune in training (e.g. NER, POS, etc)'''	)
       parser.add_argument(
           '''--max_seq_length'''       ,   default=128       ,   type=__snake_case       ,   help=(
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )       ,   )

       parser.add_argument(
           '''--labels'''       ,   default=''''''       ,   type=__snake_case       ,   help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''       ,   )
       parser.add_argument(
           '''--gpus'''       ,   default=0       ,   type=__snake_case       ,   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


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       __UpperCamelCase				:  Dict      					=							NERTransformer.add_model_specific_args(parser, os.getcwd())
       __UpperCamelCase				:  Tuple      					=							parser.parse_args()
       __UpperCamelCase				:  Optional[Any]      					=							NERTransformer(args)
       __UpperCamelCase				:  int      					=							generic_train(model, args)

       if args.do_predict:
              # See https://github.com/huggingface/transformers/issues/3159
              # pl use this default format to create a checkpoint:
              # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
              # /pytorch_lightning/callbacks/model_checkpoint.py#L322
              __UpperCamelCase				:  Optional[int]      					=							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
              __UpperCamelCase				:  List[Any]      					=							model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)