





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


lowercase				:	Union[str, Any]      						=  logging.getLogger(__name__)





@dataclass
class 			A__   :




			"""simple docstring"""



			__A       :    str	  =  field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
			__A       :    Optional[str]	  =  field(
			    default=__UpperCAmelCase			,  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=__UpperCAmelCase			,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
			__A       :    bool	  =  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.
			__A       :    Optional[str]	  =  field(
			    default=__UpperCAmelCase			,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,  )





@dataclass
class 			A__   :




			"""simple docstring"""



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



def A_     (		)  ->		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.

							a__						:     List[str]			=    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.
														a__						:     int			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
							else:
														a__						:     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.'    )

							a__						:     Optional[Any]			=    import_module('tasks'    )
							try:
														a__						:     List[Any]			=    getattr(A__		,				model_args.task_type    )
														a__						:     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 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'		,				A__    )

							# Set seed
							set_seed(training_args.seed    )

							# Prepare CONLL-2003 task
							a__						:     Tuple			=    token_classification_task.get_labels(data_args.labels    )
							a__						:     Dict[int, str]			=    dict(enumerate(A__    )    )
							a__						:     Union[str, 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.

							a__						:     Optional[Any]			=    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		,				)
							a__						:     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		,				)
							a__						:     List[Any]			=    AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path    )		,				config=A__		,				cache_dir=model_args.cache_dir		,				)

							# Get datasets
							a__						:     int			=    (
							    TokenClassificationDataset(
							        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
							)
							a__						:     Optional[int]			=    (
							    TokenClassificationDataset(
							        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__		,				A__    ) -> Tuple[List[int], List[int]]:
														a__						:     Union[str, Any]			=    np.argmax(A__		,				axis=2    )

														a__						:     Dict			=    preds.shape

														a__						:     Union[str, Any]			=    [[] for _ in range(A__    )]
														a__						:     Optional[int]			=    [[] for _ in range(A__    )]

														for i in range(A__    ):
																					for j in range(A__    ):
																												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(A__    ) -> Dict:
														a__						:     Union[str, Any]			=    align_predictions(p.predictions		,				p.label_ids    )
														return {
														    "accuracy_score": accuracy_score(A__		,				A__    ),
														    "precision": precision_score(A__		,				A__    ),
														    "recall": recall_score(A__		,				A__    ),
														    "f1": fa_score(A__		,				A__    ),
														}

							# Data collator
							a__						:     Union[str, Any]			=    DataCollatorWithPadding(A__		,				pad_to_multiple_of=8    ) if training_args.fpaa else None

							# Initialize our Trainer
							a__						:     List[str]			=    Trainer(
							    model=A__		,				args=A__		,				train_dataset=A__		,				eval_dataset=A__		,				compute_metrics=A__		,				data_collator=A__		,				)

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

														a__						:     Optional[Any]			=    trainer.evaluate()

														a__						:     List[Any]			=    os.path.join(training_args.output_dir		,				'eval_results.txt'    )
														if trainer.is_world_process_zero():
																					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:
														a__						:     Optional[Any]			=    TokenClassificationDataset(
														    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		,				)

														a__						:     Any			=    trainer.predict(A__    )
														a__						:     Union[str, Any]			=    align_predictions(A__		,				A__    )

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

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

							return results



def A_     (		A__    )  ->		Tuple:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
				main()









import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     str			=    seq_length
          a__						:     int			=    is_training
          a__						:     str			=    use_attention_mask
          a__						:     List[str]			=    use_token_type_ids
          a__						:     Optional[Any]			=    use_labels
          a__						:     List[Any]			=    vocab_size
          a__						:     Tuple			=    hidden_size
          a__						:     Dict			=    num_hidden_layers
          a__						:     List[str]			=    num_attention_heads
          a__						:     int			=    intermediate_size
          a__						:     Any			=    hidden_act
          a__						:     Optional[int]			=    hidden_dropout_prob
          a__						:     Tuple			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Optional[int]			=    type_vocab_size
          a__						:     List[Any]			=    type_sequence_label_size
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     str			=    num_choices

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







          '''simple docstring'''

          a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Dict			=    None
          if self.use_attention_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Dict			=    None
          if self.use_token_type_ids:
                 a__						:     int			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     Dict			=    BertConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

          return config, input_ids, token_type_ids, attention_mask

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







          '''simple docstring'''

          a__						:     Tuple			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     Optional[int]			=    config_and_inputs
          a__						:     str			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     int			=    config_and_inputs

          a__						:     str			=    True
          a__						:     Any			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


