



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							       =    logging.getLogger(__name__)

@dataclass
class        __snake_case					:







							'''simple docstring'''






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




@dataclass
class        __snake_case					:







							'''simple docstring'''






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




def 	A_					(		):

				"""simple docstring"""
				# 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.
				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(__a   : Any       ,						__a   : Optional[Any]						) -> 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(__a   : List[Any]						) -> 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()







def 	UpperCAmelCase_    (	__UpperCamelCase	):
    if not isinstance(__UpperCamelCase,				__UpperCamelCase	):
        raise ValueError("""double_factorial() only accepts integral values"""	)
    if n < 0:
        raise ValueError("""double_factorial() not defined for negative values"""	)
    return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






