






'''simple docstring'''




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


__lowerCamelCase   :					str   							=					logging.getLogger(__name__)







@dataclass
class    A_	:
							"""simple docstring"""




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







@dataclass
class    A_	:
							"""simple docstring"""




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

def 	__UpperCAmelCase       ( )->    List[str]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       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.
						snake_case_,	snake_case_,	snake_case_				:					str   =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
			else:
						snake_case_,	snake_case_,	snake_case_				:					List[Any]   =       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."						)

			snake_case_				:					str   =       import_module("tasks"						)
			try:
						snake_case_				:					Dict   =       getattr(__magic_name__     ,model_args.task_type						)
						snake_case_				:					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 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"     ,__magic_name__						)

			# Set seed
			set_seed(training_args.seed						)

			# Prepare CONLL-2003 task
			snake_case_				:					Any   =       token_classification_task.get_labels(data_args.labels						)
			snake_case_				:					Dict[int, str]   =       dict(enumerate(__magic_name__						)						)
			snake_case_				:					List[str]   =       len(__magic_name__						)

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

			snake_case_				:					List[Any]   =       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,num_labels=__magic_name__     ,idalabel=__magic_name__     ,labelaid={label: i for i, label in enumerate(__magic_name__						)}     ,cache_dir=model_args.cache_dir     ,)
			snake_case_				:					Optional[Any]   =       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     ,)
			snake_case_				:					Any   =       AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path     ,from_tf=bool(".ckpt" in model_args.model_name_or_path						)     ,config=__magic_name__     ,cache_dir=model_args.cache_dir     ,)

			# Get datasets
			snake_case_				:					str   =       (
			    TokenClassificationDataset(
			        token_classification_task=__magic_name__     ,data_dir=data_args.data_dir     ,tokenizer=__magic_name__     ,labels=__magic_name__     ,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
			)
			snake_case_				:					List[str]   =       (
			    TokenClassificationDataset(
			        token_classification_task=__magic_name__     ,data_dir=data_args.data_dir     ,tokenizer=__magic_name__     ,labels=__magic_name__     ,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(__magic_name__     ,__magic_name__						) -> Tuple[List[int], List[int]]:
						snake_case_				:					Dict   =       np.argmax(__magic_name__     ,axis=2						)

						snake_case_,	snake_case_				:					Dict   =       preds.shape

						snake_case_				:					Tuple   =       [[] for _ in range(__magic_name__						)]
						snake_case_				:					Tuple   =       [[] for _ in range(__magic_name__						)]

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

			# Data collator
			snake_case_				:					Optional[int]   =       DataCollatorWithPadding(__magic_name__     ,pad_to_multiple_of=8						) if training_args.fpaa else None

			# Initialize our Trainer
			snake_case_				:					Tuple   =       Trainer(
			    model=__magic_name__     ,args=__magic_name__     ,train_dataset=__magic_name__     ,eval_dataset=__magic_name__     ,compute_metrics=__magic_name__     ,data_collator=__magic_name__     ,)

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

						snake_case_				:					Tuple   =       trainer.evaluate()

						snake_case_				:					int   =       os.path.join(training_args.output_dir     ,"eval_results.txt"						)
						if trainer.is_world_process_zero():
									with open(__magic_name__     ,"w"						) as writer:
												logger.info("***** Eval results *****"						)
												for key, value in result.items():
															logger.info("  %s = %s"     ,__magic_name__     ,__magic_name__						)
															writer.write("%s = %s\n" % (key, value)						)

									results.update(__magic_name__						)

    # Predict
			if training_args.do_predict:
						snake_case_				:					List[str]   =       TokenClassificationDataset(
						    token_classification_task=__magic_name__     ,data_dir=data_args.data_dir     ,tokenizer=__magic_name__     ,labels=__magic_name__     ,model_type=config.model_type     ,max_seq_length=data_args.max_seq_length     ,overwrite_cache=data_args.overwrite_cache     ,mode=Split.test     ,)

						snake_case_,	snake_case_,	snake_case_				:					str   =       trainer.predict(__magic_name__						)
						snake_case_,	snake_case_				:					List[Any]   =       align_predictions(__magic_name__     ,__magic_name__						)

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

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

			return results

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			main()


if __name__ == "__main__":
		main()












'''simple docstring'''




from scipy.stats import spearmanr

import datasets


__lowerCamelCase   :					str   							=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__lowerCamelCase   :					int   							=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__lowerCamelCase   :					List[str]   							=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION							)
class    A_	(datasets.Metric							):
							"""simple docstring"""




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


										'''simple docstring'''




										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
										        {
										            "predictions": datasets.Value("float"				),
										            "references": datasets.Value("float"				),
										        }				)       ,							reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]       ,							)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




