




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()


lowercase_		=							logging.getLogger(__name__)





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





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







def  lowerCAmelCase  (	)  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
					__magic_name__   , __magic_name__   , __magic_name__					:							Union[str, Any]		   =							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.'''       )

					__magic_name__					:							List[str]		   =							import_module('''tasks'''       )

					try:
										__magic_name__					:							List[str]		   =							getattr(UpperCAmelCase,	model_args.task_type       )
										__magic_name__					:							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''',	UpperCAmelCase       )

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

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

					__magic_name__					:							List[str]		   =							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,	)
					__magic_name__					:							List[str]		   =							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():
										__magic_name__					:							List[str]		   =							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
					__magic_name__					:							Tuple		   =							(
					    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
					)
					__magic_name__					:							List[Any]		   =							(
					    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]]:
										__magic_name__					:							Optional[int]		   =							np.argmax(UpperCAmelCase,	axis=2       )
										__magic_name__   , __magic_name__					:							Any		   =							preds.shape
										__magic_name__					:							Optional[int]		   =							[[] for _ in range(UpperCAmelCase       )]
										__magic_name__					:							Union[str, Any]		   =							[[] 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:
										__magic_name__   , __magic_name__					:							Optional[int]		   =							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
					__magic_name__					:							Any		   =							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
					__magic_name__					:							List[str]		   =							{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''       )

										__magic_name__					:							List[str]		   =							trainer.evaluate()
										__magic_name__					:							Tuple		   =							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:
										__magic_name__					:							int		   =							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,	)

										__magic_name__   , __magic_name__   , __magic_name__					:							List[str]		   =							trainer.predict(test_dataset.get_dataset()       )
										__magic_name__   , __magic_name__					:							int		   =							align_predictions(UpperCAmelCase,	UpperCAmelCase       )
										__magic_name__					:							List[str]		   =							classification_report(UpperCAmelCase,	UpperCAmelCase       )

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

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

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

										# Save predictions
										__magic_name__					:							List[str]		   =							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:
																				__magic_name__					:							str		   =							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]:
																														__magic_name__					:							Dict		   =							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()









import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



