






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()


__UpperCAmelCase										=		logging.getLogger(__name__)



@dataclass
class 						lowerCamelCase   :







  '''simple docstring'''



  _snake_case					:					str 		=						field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
  _snake_case					:					Optional[str] 		=						field(
      default=_snake_case				,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
  _snake_case					:					Optional[str] 		=						field(
      default='''NER'''				,						metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}    )
  _snake_case					:					Optional[str] 		=						field(
      default=_snake_case				,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
  _snake_case					:					bool 		=						field(default=_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.
  _snake_case					:					Optional[str] 		=						field(
      default=_snake_case				,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,						)





@dataclass
class 						lowerCamelCase   :







  '''simple docstring'''



  _snake_case					:					str 		=						field(
      metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}    )
  _snake_case					:					Optional[str] 		=						field(
      metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}    )
  _snake_case					:					int 		=						field(
      default=1_2_8				,						metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }				,						)
  _snake_case					:					bool 		=						field(
      default=_snake_case				,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )





def   lowercase__   (  ):


     '''simple docstring'''


     UpperCAmelCase_					:   Tuple   =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
     UpperCAmelCase_					:   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.'						)

     UpperCAmelCase_					:   str   =		import_module('tasks'						)

     try:
          UpperCAmelCase_					:   Dict   =		getattr(_UpperCAmelCase  , model_args.task_type						)
          UpperCAmelCase_					:   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'  , _UpperCAmelCase						)

     # Prepare Token Classification task
     UpperCAmelCase_					:   Tuple   =		token_classification_task.get_labels(data_args.labels						)
     UpperCAmelCase_					:   Dict[int, str]   =		dict(enumerate(_UpperCAmelCase						)						)
     UpperCAmelCase_					:   Dict   =		len(_UpperCAmelCase						)

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

     UpperCAmelCase_					:   Optional[Any]   =		AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path  , num_labels=_UpperCAmelCase  , idalabel=_UpperCAmelCase  , labelaid={label: i for i, label in enumerate(_UpperCAmelCase						)}  , cache_dir=model_args.cache_dir  , )
     UpperCAmelCase_					:   Any   =		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_					:   Optional[int]   =		TFAutoModelForTokenClassification.from_pretrained(
              model_args.model_name_or_path  , from_pt=bool('.bin' in model_args.model_name_or_path						)  , config=_UpperCAmelCase  , cache_dir=model_args.cache_dir  , )

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

          for i in range(_UpperCAmelCase						):
               for j in range(_UpperCAmelCase						):
                    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(__snake_case					:							Tuple						) -> Dict:
          UpperCAmelCase_					:   str   =		align_predictions(p.predictions  , p.label_ids						)

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

     # Initialize our Trainer
     UpperCAmelCase_					:   Tuple   =		TFTrainer(
         model=_UpperCAmelCase  , args=_UpperCAmelCase  , train_dataset=train_dataset.get_dataset() if train_dataset else None  , eval_dataset=eval_dataset.get_dataset() if eval_dataset else None  , compute_metrics=_UpperCAmelCase  , )

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

     # Evaluation
     UpperCAmelCase_					:   List[str]   =		{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'						)

          UpperCAmelCase_					:   Tuple   =		trainer.evaluate()
          UpperCAmelCase_					:   Optional[int]   =		os.path.join(training_args.output_dir  , 'eval_results.txt'						)

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

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

               results.update(_UpperCAmelCase						)

    # Predict
     if training_args.do_predict:
          UpperCAmelCase_					:   List[Any]   =		TFTokenClassificationDataset(
              token_classification_task=_UpperCAmelCase  , data_dir=data_args.data_dir  , tokenizer=_UpperCAmelCase  , labels=_UpperCAmelCase  , model_type=config.model_type  , max_seq_length=data_args.max_seq_length  , overwrite_cache=data_args.overwrite_cache  , mode=Split.test  , )

          UpperCAmelCase_					:   Optional[int]   =		trainer.predict(test_dataset.get_dataset()						)
          UpperCAmelCase_					:   List[str]   =		align_predictions(_UpperCAmelCase  , _UpperCAmelCase						)
          UpperCAmelCase_					:   int   =		classification_report(_UpperCAmelCase  , _UpperCAmelCase						)

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

          UpperCAmelCase_					:   Optional[int]   =		os.path.join(training_args.output_dir  , 'test_results.txt'						)

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

          # Save predictions
          UpperCAmelCase_					:   int   =		os.path.join(training_args.output_dir  , 'test_predictions.txt'						)

          with open(_UpperCAmelCase  , 'w'						) as writer:
               with open(os.path.join(data_args.data_dir  , 'test.txt'						)  , 'r'						) as f:
                    UpperCAmelCase_					:   int   =		0

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

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

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

     return results


if __name__ == "__main__":
  main()














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






