


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


UpperCAmelCase					:  Optional[int]			=						logging.getLogger(__name__)







@dataclass
class     __lowercase    :



		"""simple docstring"""

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







@dataclass
class     __lowercase    :



		"""simple docstring"""

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






def      __lowerCamelCase		(				):





			'''simple docstring'''




			lowerCamelCase  						=   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.
						lowerCamelCase  						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
			else:
						lowerCamelCase  						=   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."""	)

			lowerCamelCase  						=   import_module("""tasks"""	)
			try:
						lowerCamelCase  						=   getattr(UpperCAmelCase_   ,				model_args.task_type	)
						lowerCamelCase  						=   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"""   ,				UpperCAmelCase_	)

			# Set seed
			set_seed(training_args.seed	)

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

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

			lowerCamelCase  						=   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   ,				)
			lowerCamelCase  						=   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   ,				)
			lowerCamelCase  						=   AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path   ,				from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)   ,				config=UpperCAmelCase_   ,				cache_dir=model_args.cache_dir   ,				)

			# Get datasets
			lowerCamelCase  						=   (
			    TokenClassificationDataset(
			        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
			)
			lowerCamelCase  						=   (
			    TokenClassificationDataset(
			        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(lowerCamelCase__						:		np.ndarray   ,				lowerCamelCase__						:		np.ndarray	) -> Tuple[List[int], List[int]]:
						lowerCamelCase  						=   np.argmax(UpperCAmelCase_   ,				axis=2	)

						lowerCamelCase  						=   preds.shape

						lowerCamelCase  						=   [[] for _ in range(UpperCAmelCase_	)]
						lowerCamelCase  						=   [[] for _ in range(UpperCAmelCase_	)]

						for i in range(UpperCAmelCase_	):
									for j in range(UpperCAmelCase_	):
												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(lowerCamelCase__						:		EvalPrediction	) -> Dict:
						lowerCamelCase  						=   align_predictions(p.predictions   ,				p.label_ids	)
						return {
						    "accuracy_score": accuracy_score(UpperCAmelCase_   ,				UpperCAmelCase_	),
						    "precision": precision_score(UpperCAmelCase_   ,				UpperCAmelCase_	),
						    "recall": recall_score(UpperCAmelCase_   ,				UpperCAmelCase_	),
						    "f1": fa_score(UpperCAmelCase_   ,				UpperCAmelCase_	),
						}

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

			# Initialize our Trainer
			lowerCamelCase  						=   Trainer(
			    model=UpperCAmelCase_   ,				args=UpperCAmelCase_   ,				train_dataset=UpperCAmelCase_   ,				eval_dataset=UpperCAmelCase_   ,				compute_metrics=UpperCAmelCase_   ,				data_collator=UpperCAmelCase_   ,				)

			# 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
			lowerCamelCase  						=   {}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)

						lowerCamelCase  						=   trainer.evaluate()

						lowerCamelCase  						=   os.path.join(training_args.output_dir   ,				"""eval_results.txt"""	)
						if trainer.is_world_process_zero():
									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:
						lowerCamelCase  						=   TokenClassificationDataset(
						    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   ,				)

						lowerCamelCase  						=   trainer.predict(UpperCAmelCase_	)
						lowerCamelCase  						=   align_predictions(UpperCAmelCase_   ,				UpperCAmelCase_	)

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

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

			return results






def      __lowerCamelCase		(				lowerCamelCase__						:		List[Any]	):





			'''simple docstring'''




			main()


if __name__ == "__main__":
		main()





def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      while b:
            a							,				a						:Optional[Any] 							=       b, a % b
      return a
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_ ,  a % b						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 ,  5						)}'''						)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 ,  3						)}'''						)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 ,  3						)}'''						)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 ,  6						)}'''						)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 ,  3						)}'''						)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,  5						)}'''						)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,  6						)}'''						)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,  3						)}'''						)


if __name__ == "__main__":
     main()





