



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()


UpperCAmelCase__			:   Tuple      			=    logging.getLogger(__name__)





@dataclass
class 							lowerCAmelCase_		:
		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=lowercase_							,		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=lowercase_							,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
		SCREAMING_SNAKE_CASE_       :			bool					       =	field(default=lowercase_							,		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=lowercase_							,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,		)





@dataclass
class 							lowerCAmelCase_		:
		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=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."""
		        )
		    }							,		)
		SCREAMING_SNAKE_CASE_       :			bool					       =	field(
		    default=lowercase_							,		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.
				_UpperCAmelCase						:  Any     					=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
				_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase						:  Dict     					=	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.'''			)

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

				try:
								_UpperCAmelCase						:  Union[str, Any]     					=	getattr(_UpperCamelCase      ,					model_args.task_type			)
								_UpperCAmelCase						:  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
				_UpperCAmelCase						:  Union[str, Any]     					=	token_classification_task.get_labels(data_args.labels			)
				_UpperCAmelCase						:  Dict[int, str]     					=	dict(enumerate(_UpperCamelCase			)			)
				_UpperCAmelCase						:  int     					=	len(_UpperCamelCase			)

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

				_UpperCAmelCase						:  int     					=	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      ,					)
				_UpperCAmelCase						:  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():
								_UpperCAmelCase						:  Optional[Any]     					=	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
				_UpperCAmelCase						:  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.train      ,					)
				    if training_args.do_train
				    else None
				)
				_UpperCAmelCase						:  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]]:
								_UpperCAmelCase						:  Dict     					=	np.argmax(_UpperCamelCase      ,					axis=2			)
								_UpperCAmelCase					, _UpperCAmelCase						:  Optional[Any]     					=	preds.shape
								_UpperCAmelCase						:  str     					=	[[] for _ in range(_UpperCamelCase			)]
								_UpperCAmelCase						:  Tuple     					=	[[] 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:
								_UpperCAmelCase					, _UpperCAmelCase						:  List[str]     					=	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
				_UpperCAmelCase						:  Dict     					=	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
				_UpperCAmelCase						:  int     					=	{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''			)

								_UpperCAmelCase						:  str     					=	trainer.evaluate()
								_UpperCAmelCase						:  Any     					=	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:
								_UpperCAmelCase						:  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.test      ,					)

								_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	trainer.predict(test_dataset.get_dataset()			)
								_UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	align_predictions(_UpperCamelCase      ,					_UpperCamelCase			)
								_UpperCAmelCase						:  Union[str, Any]     					=	classification_report(_UpperCamelCase      ,					_UpperCamelCase			)

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

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

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

								# Save predictions
								_UpperCAmelCase						:  Union[str, Any]     					=	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:
																_UpperCAmelCase						:  Union[str, Any]     					=	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]:
																								_UpperCAmelCase						:  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 math
from timeit import timeit


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Tuple     					=	abs(_UpperCamelCase			)
				while True:
								_UpperCAmelCase						:  str     					=	n // 10
								digits += 1
								if n == 0:
												break
				return digits


def 	_A	(   _UpperCamelCase			):
				return 1 if n == 0 else math.floor(math.log(abs(_UpperCamelCase			)      ,					10			) + 1			)


def 	_A	(   _UpperCamelCase			):
				return len(str(abs(_UpperCamelCase			)			)			)


def 	_A	(   ):
				from collections.abc import Callable

				def benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			) -> None:
								_UpperCAmelCase						:  List[str]     					=	F'''{func.__name__}({value})'''
								_UpperCAmelCase						:  Any     					=	timeit(F'''__main__.{call}'''      ,					setup='''import __main__'''			)
								print(F'''{call}: {func(_UpperCamelCase			)} -- {timing} seconds'''			)

				for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
								for func in (num_digits, num_digits_fast, num_digits_faster):
												benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			)
								print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
