






"""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()


__A		:	Optional[int]      =		logging.getLogger(__name__)





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





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



def 						lowercase   (    ):
		# 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.
		lowercase_			:			int								=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
		lowercase_			:			str								=       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.'''     )

		lowercase_			:			Tuple								=       import_module('''tasks'''     )

		try:
				lowercase_			:			Optional[int]								=       getattr(__snake_case		, model_args.task_type     )
				lowercase_			:			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		, )
		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'''		, __snake_case     )

		# Prepare Token Classification task
		lowercase_			:			Dict								=       token_classification_task.get_labels(data_args.labels     )
		lowercase_			:			Dict[int, str]								=       dict(enumerate(__snake_case     )     )
		lowercase_			:			Dict								=       len(__snake_case     )

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

		lowercase_			:			Dict								=       AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid={label: i for i, label in enumerate(__snake_case     )}		, cache_dir=model_args.cache_dir		, )
		lowercase_			:			Optional[Any]								=       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():
				lowercase_			:			Any								=       TFAutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path		, from_pt=bool('''.bin''' in model_args.model_name_or_path     )		, config=__snake_case		, cache_dir=model_args.cache_dir		, )

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

				for i in range(__snake_case     ):
						for j in range(__snake_case     ):
								if label_ids[i, j] != -1_0_0:
										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(__snake_case    :			EvalPrediction     ) -> Dict:
				lowercase_			:			Optional[Any]								=       align_predictions(p.predictions		, p.label_ids     )

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

		# Initialize our Trainer
		lowercase_			:			List[Any]								=       TFTrainer(
		    model=__snake_case		, args=__snake_case		, train_dataset=train_dataset.get_dataset() if train_dataset else None		, eval_dataset=eval_dataset.get_dataset() if eval_dataset else None		, compute_metrics=__snake_case		, )

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

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

				lowercase_			:			Dict								=       trainer.evaluate()
				lowercase_			:			Tuple								=       os.path.join(training_args.output_dir		, '''eval_results.txt'''     )

				with open(__snake_case		, '''w'''     ) as writer:
						logger.info('''***** Eval results *****'''     )

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

						results.update(__snake_case     )

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

				lowercase_			:			List[str]								=       trainer.predict(test_dataset.get_dataset()     )
				lowercase_			:			List[Any]								=       align_predictions(__snake_case		, __snake_case     )
				lowercase_			:			Tuple								=       classification_report(__snake_case		, __snake_case     )

				logger.info('''\n%s'''		, __snake_case     )

				lowercase_			:			Optional[int]								=       os.path.join(training_args.output_dir		, '''test_results.txt'''     )

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

				# Save predictions
				lowercase_			:			List[str]								=       os.path.join(training_args.output_dir		, '''test_predictions.txt'''     )

				with open(__snake_case		, '''w'''     ) as writer:
						with open(os.path.join(data_args.data_dir		, '''test.txt'''     )		, '''r'''     ) as f:
								lowercase_			:			int								=       0

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

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

												writer.write(__snake_case     )
										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_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Tuple      =		['''RealmTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RealmEmbedder''',
						    '''RealmForOpenQA''',
						    '''RealmKnowledgeAugEncoder''',
						    '''RealmPreTrainedModel''',
						    '''RealmReader''',
						    '''RealmScorer''',
						    '''load_tf_weights_in_realm''',
						]
						__A		:	Union[str, Any]      =		['''RealmRetriever''']


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

						__A		:	Optional[int]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



