




'''simple docstring'''



import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_							:	List[Any]	     =		logging.getLogger(__name__)





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





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					, )
       __lowercase	:   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.'''
               )
           }					, )
       __lowercase	:   bool										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	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            =	import_module('''tasks'''						)
     try:
          __snake_case            =	getattr(_lowerCamelCase ,						model_args.task_type						)
          __snake_case            =	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 if training_args.local_rank in [-1, 0] else logging.WARN ,						)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,						training_args.local_rank ,						training_args.device ,						training_args.n_gpu ,						bool(training_args.local_rank != -1						) ,						training_args.fpaa ,						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank						):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''' ,						_lowerCamelCase						)

     # Set seed
     set_seed(training_args.seed						)

     # Prepare CONLL-2003 task
     __snake_case            =	token_classification_task.get_labels(data_args.labels						)
     __snake_case            =	dict(enumerate(_lowerCamelCase						)						)
     __snake_case            =	len(_lowerCamelCase						)

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

     __snake_case            =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCamelCase ,						idalabel=_lowerCamelCase ,						labelaid={label: i for i, label in enumerate(_lowerCamelCase						)} ,						cache_dir=model_args.cache_dir ,						)
     __snake_case            =	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 ,						)
     __snake_case            =	AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path						) ,						config=_lowerCamelCase ,						cache_dir=model_args.cache_dir ,						)

     # Get datasets
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						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            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						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(_lowerCamelCase				:  np.ndarray ,						_lowerCamelCase				:  np.ndarray						) -> Tuple[List[int], List[int]]:
          __snake_case            =	np.argmax(_lowerCamelCase ,						axis=2						)

          __snake_case       ,     __snake_case            =	preds.shape

          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]

          for i in range(_lowerCamelCase						):
               for j in range(_lowerCamelCase						):
                    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                         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(_lowerCamelCase				:  EvalPrediction						) -> Dict:
          __snake_case       ,     __snake_case            =	align_predictions(p.predictions ,						p.label_ids						)
          return {
              "accuracy_score": accuracy_score(_lowerCamelCase ,						_lowerCamelCase						),
              "precision": precision_score(_lowerCamelCase ,						_lowerCamelCase						),
              "recall": recall_score(_lowerCamelCase ,						_lowerCamelCase						),
              "f1": fa_score(_lowerCamelCase ,						_lowerCamelCase						),
          }

     # Data collator
     __snake_case            =	DataCollatorWithPadding(_lowerCamelCase ,						pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __snake_case            =	Trainer(
         model=_lowerCamelCase ,						args=_lowerCamelCase ,						train_dataset=_lowerCamelCase ,						eval_dataset=_lowerCamelCase ,						compute_metrics=_lowerCamelCase ,						data_collator=_lowerCamelCase ,						)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
               tokenizer.save_pretrained(training_args.output_dir						)

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

          __snake_case            =	trainer.evaluate()

          __snake_case            =	os.path.join(training_args.output_dir ,						'''eval_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    logger.info('''***** Eval results *****'''						)
                    for key, value in result.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

               results.update(_lowerCamelCase						)

    # Predict
     if training_args.do_predict:
          __snake_case            =	TokenClassificationDataset(
              token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						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            =	trainer.predict(_lowerCamelCase						)
          __snake_case       ,     __snake_case            =	align_predictions(_lowerCamelCase ,						_lowerCamelCase						)

          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    for key, value in metrics.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

        # Save predictions
          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_predictions.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    with open(os.path.join(data_args.data_dir ,						'''test.txt'''						) ,						'''r'''						) as f:
                         token_classification_task.write_predictions_to_file(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     return results







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> int:




     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()












'''simple docstring'''



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
            tokenizer.save_pretrained(self.tmpdirname	)
            tokenizer.save_pretrained(self.tmpdirname     ,	legacy_format=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]     ,	'''<s>'''	)
            self.assertEqual(vocab_keys[1]     ,	'''<pad>'''	)
            self.assertEqual(vocab_keys[-1]     ,	'''<mask>'''	)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            self.assertEqual((2, 6)     ,	batch.input_ids.shape	)
            self.assertEqual((2, 6)     ,	batch.attention_mask.shape	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

            __snake_case            =	'''I was born in 92000, and this is falsé.'''

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            __snake_case            =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






