
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__							:							Tuple													=     logging.getLogger(__name__)






@dataclass
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






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






@dataclass
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






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





def 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       					=     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
						__UpperCamelCase       					=     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       					=     import_module('''tasks'''	)

						try:
												__UpperCamelCase       					=     getattr(snake_case	, model_args.task_type	)
												__UpperCamelCase       					=     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
						__UpperCamelCase       					=     token_classification_task.get_labels(data_args.labels	)
						__UpperCamelCase       					=     dict(enumerate(snake_case	)	)
						__UpperCamelCase       					=     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.

						__UpperCamelCase       					=     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	, )
						__UpperCamelCase       					=     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       					=     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
						__UpperCamelCase       					=     (
						    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
						)
						__UpperCamelCase       					=     (
						    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							:				Tuple	, snake_case							:				List[str]	) -> Tuple[List[int], List[int]]:
												__UpperCamelCase       					=     np.argmax(snake_case	, axis=2	)
												__UpperCamelCase       					=     preds.shape
												__UpperCamelCase       					=     [[] for _ in range(snake_case	)]
												__UpperCamelCase       					=     [[] for _ in range(snake_case	)]

												for i in range(snake_case	):
																		for j in range(snake_case	):
																								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(snake_case							:				Union[str, Any]	) -> Dict:
												__UpperCamelCase       					=     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
						__UpperCamelCase       					=     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
						__UpperCamelCase       					=     {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''	)

												__UpperCamelCase       					=     trainer.evaluate()
												__UpperCamelCase       					=     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:
												__UpperCamelCase       					=     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	, )

												__UpperCamelCase       					=     trainer.predict(test_dataset.get_dataset()	)
												__UpperCamelCase       					=     align_predictions(snake_case	, snake_case	)
												__UpperCamelCase       					=     classification_report(snake_case	, snake_case	)

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

												__UpperCamelCase       					=     os.path.join(training_args.output_dir	, '''test_results.txt'''	)

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

												# Save predictions
												__UpperCamelCase       					=     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:
																								__UpperCamelCase       					=     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]:
																																				__UpperCamelCase       					=     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()






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					CustomConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.hidden_size       ,		config.hidden_size     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													pass






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					NoSuperInitConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.attribute       ,		config.attribute     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													pass




