




'''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


_snake_case				:				Any       							=		logging.getLogger(__name__)

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



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



def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    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					,							_a					,							_a     					=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														_a					,							_a					,							_a     					=    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     					=    import_module('''tasks'''				)
							try:
														_a     					=    getattr(UpperCamelCase			,						model_args.task_type				)
														_a     					=    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'''			,						UpperCamelCase				)

							# Set seed
							set_seed(training_args.seed				)

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

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

							_a     					=    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			,						)
							_a     					=    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     					=    AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path			,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)			,						config=UpperCamelCase			,						cache_dir=model_args.cache_dir			,						)

							# Get datasets
							_a     					=    (
							    TokenClassificationDataset(
							        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
							)
							_a     					=    (
							    TokenClassificationDataset(
							        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(UpperCamelCase	:  np.ndarray			,						UpperCamelCase	:  np.ndarray				) -> Tuple[List[int], List[int]]:
														_a     					=    np.argmax(UpperCamelCase			,						axis=2				)

														_a					,							_a     					=    preds.shape

														_a     					=    [[] for _ in range(UpperCamelCase				)]
														_a     					=    [[] for _ in range(UpperCamelCase				)]

														for i in range(UpperCamelCase				):
																					for j in range(UpperCamelCase				):
																												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(UpperCamelCase	:  EvalPrediction				) -> Dict:
														_a					,							_a     					=    align_predictions(p.predictions			,						p.label_ids				)
														return {
														    "accuracy_score": accuracy_score(UpperCamelCase			,						UpperCamelCase				),
														    "precision": precision_score(UpperCamelCase			,						UpperCamelCase				),
														    "recall": recall_score(UpperCamelCase			,						UpperCamelCase				),
														    "f1": fa_score(UpperCamelCase			,						UpperCamelCase				),
														}

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

							# Initialize our Trainer
							_a     					=    Trainer(
							    model=UpperCamelCase			,						args=UpperCamelCase			,						train_dataset=UpperCamelCase			,						eval_dataset=UpperCamelCase			,						compute_metrics=UpperCamelCase			,						data_collator=UpperCamelCase			,						)

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

														_a     					=    trainer.evaluate()

														_a     					=    os.path.join(training_args.output_dir			,						'''eval_results.txt'''				)
														if trainer.is_world_process_zero():
																					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:
														_a     					=    TokenClassificationDataset(
														    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			,						)

														_a					,							_a					,							_a     					=    trainer.predict(UpperCamelCase				)
														_a					,							_a     					=    align_predictions(UpperCamelCase			,						UpperCamelCase				)

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

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

							return results
def 		snake_case_   (UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							main()


if __name__ == "__main__":
				main()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				Dict       							=		{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		['AlbertTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		['AlbertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AlbertForMaskedLM',
				    'AlbertForMultipleChoice',
				    'AlbertForPreTraining',
				    'AlbertForQuestionAnswering',
				    'AlbertForSequenceClassification',
				    'AlbertForTokenClassification',
				    'AlbertModel',
				    'AlbertPreTrainedModel',
				    'load_tf_weights_in_albert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Any       							=		[
				    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFAlbertForMaskedLM',
				    'TFAlbertForMultipleChoice',
				    'TFAlbertForPreTraining',
				    'TFAlbertForQuestionAnswering',
				    'TFAlbertForSequenceClassification',
				    'TFAlbertForTokenClassification',
				    'TFAlbertMainLayer',
				    'TFAlbertModel',
				    'TFAlbertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Any       							=		[
				    'FlaxAlbertForMaskedLM',
				    'FlaxAlbertForMultipleChoice',
				    'FlaxAlbertForPreTraining',
				    'FlaxAlbertForQuestionAnswering',
				    'FlaxAlbertForSequenceClassification',
				    'FlaxAlbertForTokenClassification',
				    'FlaxAlbertModel',
				    'FlaxAlbertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert import AlbertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert_fast import AlbertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_albert import (
								    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AlbertForMaskedLM,
								    AlbertForMultipleChoice,
								    AlbertForPreTraining,
								    AlbertForQuestionAnswering,
								    AlbertForSequenceClassification,
								    AlbertForTokenClassification,
								    AlbertModel,
								    AlbertPreTrainedModel,
								    load_tf_weights_in_albert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_albert import (
								    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFAlbertForMaskedLM,
								    TFAlbertForMultipleChoice,
								    TFAlbertForPreTraining,
								    TFAlbertForQuestionAnswering,
								    TFAlbertForSequenceClassification,
								    TFAlbertForTokenClassification,
								    TFAlbertMainLayer,
								    TFAlbertModel,
								    TFAlbertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_albert import (
								    FlaxAlbertForMaskedLM,
								    FlaxAlbertForMultipleChoice,
								    FlaxAlbertForPreTraining,
								    FlaxAlbertForQuestionAnswering,
								    FlaxAlbertForSequenceClassification,
								    FlaxAlbertForTokenClassification,
								    FlaxAlbertModel,
								    FlaxAlbertPreTrainedModel,
								)
else:
				import sys

				_snake_case				:				Union[str, Any]       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

