


"""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()


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




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




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





def 			a_   (  ):
			# 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.
			__lowerCamelCase        =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
			__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			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."""    )

			__lowerCamelCase        =			import_module("""tasks"""    )

			try:
						__lowerCamelCase        =			getattr(lowercase__, model_args.task_type    )
						__lowerCamelCase        =			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""", lowercase__    )

			# Prepare Token Classification task
			__lowerCamelCase        =			token_classification_task.get_labels(data_args.labels    )
			__lowerCamelCase        =			dict(enumerate(lowercase__    )    )
			__lowerCamelCase        =			len(lowercase__    )

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

			__lowerCamelCase        =			AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase__, idalabel=lowercase__, labelaid={label: i for i, label in enumerate(lowercase__    )}, cache_dir=model_args.cache_dir, )
			__lowerCamelCase        =			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():
						__lowerCamelCase        =			TFAutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path, from_pt=bool(""".bin""" in model_args.model_name_or_path    ), config=lowercase__, cache_dir=model_args.cache_dir, )

			# Get datasets
			__lowerCamelCase        =			(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, 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
			)
			__lowerCamelCase        =			(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, 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(lowercase__   :np.ndarray, lowercase__   :np.ndarray    ) -> Tuple[List[int], List[int]]:
						__lowerCamelCase        =			np.argmax(lowercase__, axis=2    )
						__lowerCamelCase    ,__lowerCamelCase        =			preds.shape
						__lowerCamelCase        =			[[] for _ in range(lowercase__    )]
						__lowerCamelCase        =			[[] for _ in range(lowercase__    )]

						for i in range(lowercase__    ):
									for j in range(lowercase__    ):
												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(lowercase__   :EvalPrediction    ) -> Dict:
						__lowerCamelCase    ,__lowerCamelCase        =			align_predictions(p.predictions, p.label_ids    )

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

			# Initialize our Trainer
			__lowerCamelCase        =			TFTrainer(
			    model=lowercase__, args=lowercase__, train_dataset=train_dataset.get_dataset() if train_dataset else None, eval_dataset=eval_dataset.get_dataset() if eval_dataset else None, compute_metrics=lowercase__, )

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

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

						__lowerCamelCase        =			trainer.evaluate()
						__lowerCamelCase        =			os.path.join(training_args.output_dir, """eval_results.txt"""    )

						with open(lowercase__, """w"""    ) as writer:
									logger.info("""***** Eval results *****"""    )

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

									results.update(lowercase__    )

    # Predict
			if training_args.do_predict:
						__lowerCamelCase        =			TFTokenClassificationDataset(
						    token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.test, )

						__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			trainer.predict(test_dataset.get_dataset()    )
						__lowerCamelCase    ,__lowerCamelCase        =			align_predictions(lowercase__, lowercase__    )
						__lowerCamelCase        =			classification_report(lowercase__, lowercase__    )

						logger.info("""\n%s""", lowercase__    )

						__lowerCamelCase        =			os.path.join(training_args.output_dir, """test_results.txt"""    )

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

						# Save predictions
						__lowerCamelCase        =			os.path.join(training_args.output_dir, """test_predictions.txt"""    )

						with open(lowercase__, """w"""    ) as writer:
									with open(os.path.join(data_args.data_dir, """test.txt"""    ), """r"""    ) as f:
												__lowerCamelCase        =			0

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

																		if not preds_list[example_id]:
																					example_id += 1
															elif preds_list[example_id]:
																		__lowerCamelCase        =			line.split()[0] + """ """ + preds_list[example_id].pop(0    ) + """\n"""

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

			return results


if __name__ == "__main__":
					main()








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__	:   Optional[Any]          =							{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[Any]          =							[
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   Tuple          =							[
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




