





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()


lowerCAmelCase_   =   logging.getLogger(__name__)

@dataclass
class 				A    :
      _SCREAMING_SNAKE_CASE					=							field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
      _SCREAMING_SNAKE_CASE					=							field(
          default=__UpperCAmelCase       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
      _SCREAMING_SNAKE_CASE					=							field(
          default="""NER"""       ,metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}				)
      _SCREAMING_SNAKE_CASE					=							field(
          default=__UpperCAmelCase       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
      _SCREAMING_SNAKE_CASE					=							field(default=__UpperCAmelCase       ,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.
      _SCREAMING_SNAKE_CASE					=							field(
          default=__UpperCAmelCase       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)


@dataclass
class 				A    :
      _SCREAMING_SNAKE_CASE					=							field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
      _SCREAMING_SNAKE_CASE					=							field(
          metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				)
      _SCREAMING_SNAKE_CASE					=							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."""
              )
          }       ,)
      _SCREAMING_SNAKE_CASE					=							field(
          default=__UpperCAmelCase       ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)




def 						A_			(  )			->						str:
       # 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.
       _snake_case      :							Any     				=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
       _snake_case				,    _snake_case				,    _snake_case      :							List[Any]     				=						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.'''  )

       _snake_case      :							Optional[Any]     				=						import_module('''tasks'''  )

       try:
              _snake_case      :							List[Any]     				=						getattr(lowercase_      ,		model_args.task_type  )
              _snake_case      :							TokenClassificationTask     				=						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'''      ,		lowercase_  )

       # Prepare Token Classification task
       _snake_case      :							Optional[int]     				=						token_classification_task.get_labels(data_args.labels  )
       _snake_case      :							Dict[int, str]     				=						dict(enumerate(lowercase_  )  )
       _snake_case      :							Any     				=						len(lowercase_  )

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

       _snake_case      :							Dict     				=						AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,		num_labels=lowercase_      ,		idalabel=lowercase_      ,		labelaid={label: i for i, label in enumerate(lowercase_  )}      ,		cache_dir=model_args.cache_dir      ,		)
       _snake_case      :							Optional[int]     				=						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():
              _snake_case      :							List[str]     				=						TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path      ,		from_pt=bool('''.bin''' in model_args.model_name_or_path  )      ,		config=lowercase_      ,		cache_dir=model_args.cache_dir      ,		)

       # Get datasets
       _snake_case      :							Dict     				=						(
           TFTokenClassificationDataset(
               token_classification_task=lowercase_      ,		data_dir=data_args.data_dir      ,		tokenizer=lowercase_      ,		labels=lowercase_      ,		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
       )
       _snake_case      :							List[Any]     				=						(
           TFTokenClassificationDataset(
               token_classification_task=lowercase_      ,		data_dir=data_args.data_dir      ,		tokenizer=lowercase_      ,		labels=lowercase_      ,		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(lowercase_      ,		lowercase_  ) -> Tuple[List[int], List[int]]:
              _snake_case      :							str     				=						np.argmax(lowercase_      ,		axis=2  )
              _snake_case				,    _snake_case      :							Optional[int]     				=						preds.shape
              _snake_case      :							Any     				=						[[] for _ in range(lowercase_  )]
              _snake_case      :							List[str]     				=						[[] for _ in range(lowercase_  )]

              for i in range(lowercase_  ):
                     for j in range(lowercase_  ):
                            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(lowercase_  ) -> Dict:
              _snake_case				,    _snake_case      :							Tuple     				=						align_predictions(p.predictions      ,		p.label_ids  )

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

       # Initialize our Trainer
       _snake_case      :							Dict     				=						TFTrainer(
           model=lowercase_      ,		args=lowercase_      ,		train_dataset=train_dataset.get_dataset() if train_dataset else None      ,		eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,		compute_metrics=lowercase_      ,		)

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

       # Evaluation
       _snake_case      :							str     				=						{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''  )

              _snake_case      :							List[str]     				=						trainer.evaluate()
              _snake_case      :							Optional[Any]     				=						os.path.join(training_args.output_dir      ,		'''eval_results.txt'''  )

              with open(lowercase_      ,		'''w'''  ) as writer:
                     logger.info('''***** Eval results *****'''  )

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

                     results.update(lowercase_  )

    # Predict
       if training_args.do_predict:
              _snake_case      :							Union[str, Any]     				=						TFTokenClassificationDataset(
                  token_classification_task=lowercase_      ,		data_dir=data_args.data_dir      ,		tokenizer=lowercase_      ,		labels=lowercase_      ,		model_type=config.model_type      ,		max_seq_length=data_args.max_seq_length      ,		overwrite_cache=data_args.overwrite_cache      ,		mode=Split.test      ,		)

              _snake_case				,    _snake_case				,    _snake_case      :							Dict     				=						trainer.predict(test_dataset.get_dataset()  )
              _snake_case				,    _snake_case      :							List[Any]     				=						align_predictions(lowercase_      ,		lowercase_  )
              _snake_case      :							List[Any]     				=						classification_report(lowercase_      ,		lowercase_  )

              logger.info('''\n%s'''      ,		lowercase_  )

              _snake_case      :							Any     				=						os.path.join(training_args.output_dir      ,		'''test_results.txt'''  )

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

              # Save predictions
              _snake_case      :							Union[str, Any]     				=						os.path.join(training_args.output_dir      ,		'''test_predictions.txt'''  )

              with open(lowercase_      ,		'''w'''  ) as writer:
                     with open(os.path.join(data_args.data_dir      ,		'''test.txt'''  )      ,		'''r'''  ) as f:
                            _snake_case      :							List[str]     				=						0

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

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

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

       return results


if __name__ == "__main__":
   main()













from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
       # The first operation in a checkpoint can't be in-place, but it's
       # nice to have in-place addition during inference. Thus...
       if not inplace:
              _snake_case      :							Any     				=						ma + ma
       else:
              ma += ma

       return ma




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       _snake_case      :							str     				=						-1 * len(lowercase_  )
       _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
       return tensor.permute(first_inds + [zero_index + i for i in inds]  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       return t.reshape(t.shape[:-no_dims] + (-1,)  )




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
       _snake_case      :							Any     				=						mask.expand(*value.shape  )
       return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))




def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
       _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
       _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
       return torch.bucketize(lowercase_      ,		lowercase_  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
       _snake_case      :							Dict     				=						dicts[0]
       _snake_case      :							str     				=						{}
       for k, v in first.items():
              _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
              if isinstance(lowercase_      ,		lowercase_  ):
                     _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
              else:
                     _snake_case      :							Dict     				=						fn(lowercase_  )

       return new_dict




def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
       _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
       _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
       _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
       return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
       _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
       for i, s in enumerate(data.shape[:no_batch_dims]  ):
              _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
              _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
              ranges.append(lowercase_  )

       _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
       _snake_case      :							Union[str, Any]     				=						inds
       ranges.extend(lowercase_  )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(lowercase_  )]


lowerCAmelCase_   =   TypeVar("T")




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
       _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
       for k, v in dic.items():
              if isinstance(lowercase_      ,		lowercase_  ):
                     _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
              else:
                     _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )

       return new_dict




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
       ...




@overload
def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
       ...




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
       if isinstance(lowercase_      ,		lowercase_  ):
              return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
       elif isinstance(lowercase_      ,		lowercase_  ):
              return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
       elif isinstance(lowercase_      ,		lowercase_  ):
              return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
       elif isinstance(lowercase_      ,		lowercase_  ):
              return fn(lowercase_  )
       else:
              print(type(lowercase_  )  )
              raise ValueError('''Not supported'''  )
lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)






