


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_    :					Dict             =logging.getLogger(__name__)





@dataclass
class        lowercase_				:






							"""simple docstring"""







							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        lowercase_				:






							"""simple docstring"""







							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=1_28			,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   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
						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       ,    )
						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"""       ,    UpperCAmelCase__      )

						# Prepare Token Classification 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       ,    )

						with training_args.strategy.scope():
												a_					=		TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path       ,    from_pt=bool(""".bin""" in model_args.model_name_or_path      )       ,    config=UpperCAmelCase__       ,    cache_dir=model_args.cache_dir       ,    )

						# Get datasets
						a_					=		(
						    TFTokenClassificationDataset(
						        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_					=		(
						    TFTokenClassificationDataset(
						        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__       ,    UpperCAmelCase__      ) -> 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] != -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(UpperCAmelCase__      ) -> Dict:
												a_			,   a_					=		align_predictions(p.predictions       ,    p.label_ids      )

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

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

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												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"""      )

												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_					=		TFTokenClassificationDataset(
												    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(test_dataset.get_dataset()      )
												a_			,   a_					=		align_predictions(UpperCAmelCase__       ,    UpperCAmelCase__      )
												a_					=		classification_report(UpperCAmelCase__       ,    UpperCAmelCase__      )

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

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

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

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

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

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

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

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

						return results


if __name__ == "__main__":
	main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    :					Optional[Any]             ={
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_    :					Union[str, Any]             =[
	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Wav2Vec2ConformerForAudioFrameClassification""",
	    """Wav2Vec2ConformerForCTC""",
	    """Wav2Vec2ConformerForPreTraining""",
	    """Wav2Vec2ConformerForSequenceClassification""",
	    """Wav2Vec2ConformerForXVector""",
	    """Wav2Vec2ConformerModel""",
	    """Wav2Vec2ConformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_wavaveca_conformer import (
	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    WavaVecaConformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca_conformer import (
		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaConformerForAudioFrameClassification,
		    WavaVecaConformerForCTC,
		    WavaVecaConformerForPreTraining,
		    WavaVecaConformerForSequenceClassification,
		    WavaVecaConformerForXVector,
		    WavaVecaConformerModel,
		    WavaVecaConformerPreTrainedModel,
		)

else:
	import sys

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