
import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE							:int										=    logging.getLogger(__name__)





@dataclass
class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
	snake_case_    =					field(
	    default=__SCREAMING_SNAKE_CASE      ,      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=__SCREAMING_SNAKE_CASE      ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
	snake_case_    =					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.
	snake_case_    =					field(
	    default=__SCREAMING_SNAKE_CASE      ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,      )





@dataclass
class 							UpperCAmelCase				:




	'''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(
	    default=__SCREAMING_SNAKE_CASE      ,      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=__SCREAMING_SNAKE_CASE      ,      metadata={"help": "Overwrite the cached training and evaluation sets"}  )





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







				"""simple docstring"""



				__A             =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
				if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								__A      ,							__A      ,							__A             =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
				else:
								__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(a_		,       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 if training_args.local_rank in [-1, 0] else logging.WARN		,       )
				logger.warning(
				    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"		,       training_args.local_rank		,       training_args.device		,       training_args.n_gpu		,       bool(training_args.local_rank != -1						)		,       training_args.fpaa		,       )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank						):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("Training/evaluation parameters %s"		,       a_						)

				# Set seed
				set_seed(training_args.seed						)

				# Prepare CONLL-2003 task
				__A             =			token_classification_task.get_labels(data_args.labels						)
				__A             =			dict(enumerate(a_						)						)
				__A             =			len(a_						)

				# 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=a_		,       idalabel=a_		,       labelaid={label: i for i, label in enumerate(a_						)}		,       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		,       )
				__A             =			AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path		,       from_tf=bool(".ckpt" in model_args.model_name_or_path						)		,       config=a_		,       cache_dir=model_args.cache_dir		,       )

				# Get datasets
				__A             =			(
				    TokenClassificationDataset(
				        token_classification_task=a_		,       data_dir=data_args.data_dir		,       tokenizer=a_		,       labels=a_		,       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             =			(
				    TokenClassificationDataset(
				        token_classification_task=a_		,       data_dir=data_args.data_dir		,       tokenizer=a_		,       labels=a_		,       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_		,       a_						) -> Tuple[List[int], List[int]]:
								__A             =			np.argmax(a_		,       axis=2						)

								__A      ,							__A             =			preds.shape

								__A             =			[[] for _ in range(a_						)]
								__A             =			[[] for _ in range(a_						)]

								for i in range(a_						):
												for j in range(a_						):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				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_						) -> Dict:
								__A      ,							__A             =			align_predictions(p.predictions		,       p.label_ids						)
								return {
								    "accuracy_score": accuracy_score(a_		,       a_						),
								    "precision": precision_score(a_		,       a_						),
								    "recall": recall_score(a_		,       a_						),
								    "f1": fa_score(a_		,       a_						),
								}

				# Data collator
				__A             =			DataCollatorWithPadding(a_		,       pad_to_multiple_of=8						) if training_args.fpaa else None

				# Initialize our Trainer
				__A             =			Trainer(
				    model=a_		,       args=a_		,       train_dataset=a_		,       eval_dataset=a_		,       compute_metrics=a_		,       data_collator=a_		,       )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												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"						)
								if trainer.is_world_process_zero():
												with open(a_		,       "w"						) as writer:
																logger.info("***** Eval results *****"						)
																for key, value in result.items():
																				logger.info("  %s = %s"		,       a_		,       a_						)
																				writer.write("%s = %s\n" % (key, value)						)

												results.update(a_						)

    # Predict
				if training_args.do_predict:
								__A             =			TokenClassificationDataset(
								    token_classification_task=a_		,       data_dir=data_args.data_dir		,       tokenizer=a_		,       labels=a_		,       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(a_						)
								__A      ,							__A             =			align_predictions(a_		,       a_						)

								__A             =			os.path.join(training_args.output_dir		,       "test_results.txt"						)
								if trainer.is_world_process_zero():
												with open(a_		,       "w"						) as writer:
																for key, value in metrics.items():
																				logger.info("  %s = %s"		,       a_		,       a_						)
																				writer.write("%s = %s\n" % (key, value)						)

        # Save predictions
								__A             =			os.path.join(training_args.output_dir		,       "test_predictions.txt"						)
								if trainer.is_world_process_zero():
												with open(a_		,       "w"						) as writer:
																with open(os.path.join(data_args.data_dir		,       "test.txt"						)		,       "r"						) as f:
																				token_classification_task.write_predictions_to_file(a_		,       a_		,       a_						)

				return results


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				main()


if __name__ == "__main__":
					main()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Dict										=    ['EncoderDecoderModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int										=    ['TFEncoderDecoderModel']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
