






'''simple docstring'''






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




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




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Union[str, Any]:







   """simple docstring"""
   # 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					:   Dict  				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
   __snake_case				,__snake_case				,__snake_case					:   Optional[int]  				=			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					:   Dict  				=			import_module('tasks'	)

   try:
      __snake_case					:   Optional[Any]  				=			getattr(A	,		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'	,		A	)

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

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

   __snake_case					:   int  				=			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	,		)
   __snake_case					:   str  				=			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[Any]  				=			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
   __snake_case					:   int  				=			(
       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
   )
   __snake_case					:   int  				=			(
       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]]:
      __snake_case					:   int  				=			np.argmax(A	,		axis=2	)
      __snake_case				,__snake_case					:   Union[str, Any]  				=			preds.shape
      __snake_case					:   str  				=			[[] for _ in range(A	)]
      __snake_case					:   Dict  				=			[[] for _ in range(A	)]

      for i in range(A	):
         for j in range(A	):
            if label_ids[i, j] != -1_00:
               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:
      __snake_case				,__snake_case					:   Tuple  				=			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
   __snake_case					:   List[str]  				=			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
   __snake_case					:   List[str]  				=			{}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'	)

      __snake_case					:   Optional[Any]  				=			trainer.evaluate()
      __snake_case					:   int  				=			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:
      __snake_case					:   Optional[int]  				=			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	,		)

      __snake_case				,__snake_case				,__snake_case					:   Optional[Any]  				=			trainer.predict(test_dataset.get_dataset()	)
      __snake_case				,__snake_case					:   str  				=			align_predictions(A	,		A	)
      __snake_case					:   List[str]  				=			classification_report(A	,		A	)

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

      __snake_case					:   int  				=			os.path.join(training_args.output_dir	,		'test_results.txt'	)

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

      # Save predictions
      __snake_case					:   Optional[Any]  				=			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:
            __snake_case					:   str  				=			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]:
                  __snake_case					:   Dict  				=			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()







'''simple docstring'''






def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(a + 1	,		4_00	):
         for c in range(b + 1	,		5_00	):
            if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
               return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(4_00	):
         __snake_case					:   Optional[Any]  				=			10_00 - a - b
         if a < b < c and (a**2) + (b**2) == (c**2):
            return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			None:







   """simple docstring"""
   import timeit

   print(
       timeit.timeit('solution()'	,		setup='from __main__ import solution'	,		number=10_00	)	)
   print(
       timeit.timeit(
           'solution_fast()'	,		setup='from __main__ import solution_fast'	,		number=10_00	)	)


if __name__ == "__main__":
      print(f'''{solution() = }''')