



'''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()


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






@dataclass
class        UpperCAmelCase__						:
					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        UpperCAmelCase__						:
					a   :  str   =				field(
					    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
					a   :  Optional[str]   =				field(
					    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 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__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(lowerCamelCase					,		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"					,		lowerCamelCase       )

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

	# 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=lowerCamelCase					,		idalabel=lowerCamelCase					,		labelaid={label: i for i, label in enumerate(lowerCamelCase       )}					,		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=lowerCamelCase					,		cache_dir=model_args.cache_dir					,		)

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

		for i in range(lowerCamelCase       ):
			for j in range(lowerCamelCase       ):
				if label_ids[i, j] != -1_00:
					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(lowerCamelCase :    EvalPrediction       ) -> Dict:
		__lowerCAmelCase				,							__lowerCAmelCase       				=	align_predictions(p.predictions					,		p.label_ids       )

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

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

	# 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(lowerCamelCase					,		"w"       ) as writer:
			logger.info("***** Eval results *****"       )

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

			results.update(lowerCamelCase       )

    # Predict
	if training_args.do_predict:
		__lowerCAmelCase       				=	TFTokenClassificationDataset(
		    token_classification_task=lowerCamelCase					,		data_dir=data_args.data_dir					,		tokenizer=lowerCamelCase					,		labels=lowerCamelCase					,		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(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	classification_report(lowerCamelCase					,		lowerCamelCase       )

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

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

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

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

		with open(lowerCamelCase					,		"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(lowerCamelCase       )

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

	return results


if __name__ == "__main__":
	main()




'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    float					,		lowerCamelCase :    float       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	float()
	try:
		__lowerCAmelCase       				=	mass * acceleration
	except Exception:
		return -0.0
	return force


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo
	lowerCAmelCase						:		List[Any]						      = 12.5
	lowerCAmelCase						:		Optional[Any]						      = 1_0
	lowerCAmelCase						:		str						      = newtons_second_law_of_motion(mass, acceleration)
	print('''The force is ''', force, '''N''')