
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_A							=       logging.getLogger(__name__)

@dataclass
class 		lowercase_							:
 A__    :					str    =     field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
 A__    :					Optional[str]    =     field(
     default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
 A__    :					Optional[str]    =     field(
     default="""NER"""				,						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}					)
 A__    :					Optional[str]    =     field(
     default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
 A__    :					bool    =     field(default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Set this flag to use fast tokenization."""}					)
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 A__    :					Optional[str]    =     field(
     default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,						)



@dataclass
class 		lowercase_							:
 A__    :					str    =     field(
     metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}					)
 A__    :					Optional[str]    =     field(
     metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}					)
 A__    :					int    =     field(
     default=128				,						metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }				,						)
 A__    :					bool    =     field(
     default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)




def 					lowerCamelCase__					(    )		->						Dict:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       UpperCamelCase_											=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
       UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir  )
           and os.listdir(training_args.output_dir  )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                  """ --overwrite_output_dir to overcome."""  )

       UpperCamelCase_											=							import_module("""tasks"""  )

       try:
              UpperCamelCase_											=							getattr(a__						,   model_args.task_type  )
              UpperCamelCase_											=							token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                  f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''  )

       # Setup logging
       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						,   datefmt="""%m/%d/%Y %H:%M:%S"""						,   level=logging.INFO						,   )
       logger.info(
           """n_replicas: %s, distributed training: %s, 16-bits training: %s"""						,   training_args.n_replicas						,   bool(training_args.n_replicas > 1  )						,   training_args.fpaa						,   )
       logger.info("""Training/evaluation parameters %s"""						,   a__  )

       # Prepare Token Classification task
       UpperCamelCase_											=							token_classification_task.get_labels(data_args.labels  )
       UpperCamelCase_											=							dict(enumerate(a__  )  )
       UpperCamelCase_											=							len(a__  )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       UpperCamelCase_											=							AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=a__						,   idalabel=a__						,   labelaid={label: i for i, label in enumerate(a__  )}						,   cache_dir=model_args.cache_dir						,   )
       UpperCamelCase_											=							AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,   cache_dir=model_args.cache_dir						,   use_fast=model_args.use_fast						,   )

       with training_args.strategy.scope():
              UpperCamelCase_											=							TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path						,   from_pt=bool(""".bin""" in model_args.model_name_or_path  )						,   config=a__						,   cache_dir=model_args.cache_dir						,   )

       # Get datasets
       UpperCamelCase_											=							(
           TFTokenClassificationDataset(
               token_classification_task=a__						,   data_dir=data_args.data_dir						,   tokenizer=a__						,   labels=a__						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.train						,   )
           if training_args.do_train
           else None
       )
       UpperCamelCase_											=							(
           TFTokenClassificationDataset(
               token_classification_task=a__						,   data_dir=data_args.data_dir						,   tokenizer=a__						,   labels=a__						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.dev						,   )
           if training_args.do_eval
           else None
       )

       def align_predictions(a__	:	np.ndarray						,   a__	:	np.ndarray  ) -> Tuple[List[int], List[int]]:
              UpperCamelCase_											=							np.argmax(a__						,   axis=2  )
              UpperCamelCase_							,					UpperCamelCase_											=							preds.shape
              UpperCamelCase_											=							[[] for _ in range(a__  )]
              UpperCamelCase_											=							[[] for _ in range(a__  )]

              for i in range(a__  ):
                     for j in range(a__  ):
                            if label_ids[i, j] != -100:
                                   out_label_list[i].append(label_map[label_ids[i][j]]  )
                                   preds_list[i].append(label_map[preds[i][j]]  )

              return preds_list, out_label_list

       def compute_metrics(a__	:	EvalPrediction  ) -> Dict:
              UpperCamelCase_							,					UpperCamelCase_											=							align_predictions(p.predictions						,   p.label_ids  )

              return {
                  "precision": precision_score(a__						,   a__  ),
                  "recall": recall_score(a__						,   a__  ),
                  "f1": fa_score(a__						,   a__  ),
              }

       # Initialize our Trainer
       UpperCamelCase_											=							TFTrainer(
           model=a__						,   args=a__						,   train_dataset=train_dataset.get_dataset() if train_dataset else None						,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,   compute_metrics=a__						,   )

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir  )

       # Evaluation
       UpperCamelCase_											=							{}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""  )

              UpperCamelCase_											=							trainer.evaluate()
              UpperCamelCase_											=							os.path.join(training_args.output_dir						,   """eval_results.txt"""  )

              with open(a__						,   """w"""  ) as writer:
                     logger.info("""***** Eval results *****"""  )

                     for key, value in result.items():
                            logger.info("""  %s = %s"""						,   a__						,   a__  )
                            writer.write("""%s = %s\n""" % (key, value)  )

                     results.update(a__  )

    # Predict
       if training_args.do_predict:
              UpperCamelCase_											=							TFTokenClassificationDataset(
                  token_classification_task=a__						,   data_dir=data_args.data_dir						,   tokenizer=a__						,   labels=a__						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.test						,   )

              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							trainer.predict(test_dataset.get_dataset()  )
              UpperCamelCase_							,					UpperCamelCase_											=							align_predictions(a__						,   a__  )
              UpperCamelCase_											=							classification_report(a__						,   a__  )

              logger.info("""\n%s"""						,   a__  )

              UpperCamelCase_											=							os.path.join(training_args.output_dir						,   """test_results.txt"""  )

              with open(a__						,   """w"""  ) as writer:
                     writer.write("""%s\n""" % report  )

              # Save predictions
              UpperCamelCase_											=							os.path.join(training_args.output_dir						,   """test_predictions.txt"""  )

              with open(a__						,   """w"""  ) as writer:
                     with open(os.path.join(data_args.data_dir						,   """test.txt"""  )						,   """r"""  ) as f:
                            UpperCamelCase_											=							0

                            for line in f:
                                   if line.startswith("""-DOCSTART-"""  ) or line == "" or line == "\n":
                                          writer.write(a__  )

                                          if not preds_list[example_id]:
                                                 example_id += 1
                                   elif preds_list[example_id]:
                                          UpperCamelCase_											=							line.split()[0] + """ """ + preds_list[example_id].pop(0  ) + """\n"""

                                          writer.write(a__  )
                                   else:
                                          logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""						,   line.split()[0]  )

       return results


if __name__ == "__main__":
      main()





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



