




'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowerCamelCase							:						int							     =  '\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

_lowerCamelCase							:						Any							     =  '\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

_lowerCamelCase							:						str							     =  '\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,	_KWARGS_DESCRIPTION							)
class 					lowerCamelCase_							(	datasets.Metric							):




					'''simple docstring'''






					def     __SCREAMING_SNAKE_CASE     (   self			):
							return datasets.MetricInfo(
							    description=_DESCRIPTION,  citation=_CITATION,  homepage="https://github.com/chakki-works/seqeval",  inputs_description=_KWARGS_DESCRIPTION,  features=datasets.Features(
							        {
							            "predictions": datasets.Sequence(datasets.Value("string",  id="label"			),  id="sequence"			),
							            "references": datasets.Sequence(datasets.Value("string",  id="label"			),  id="sequence"			),
							        }			),  codebase_urls=["https://github.com/chakki-works/seqeval"],  reference_urls=["https://github.com/chakki-works/seqeval"],  )






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = False,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = "warn",  ):
							if scheme is not None:
									try:
											_snake_case     	=	importlib.import_module("seqeval.scheme"			)
											_snake_case     	=	getattr(__UpperCamelCase,  __UpperCamelCase			)
									except AttributeError:
											raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''			)
							_snake_case     	=	classification_report(
							    y_true=__UpperCamelCase,  y_pred=__UpperCamelCase,  suffix=__UpperCamelCase,  output_dict=__UpperCamelCase,  scheme=__UpperCamelCase,  mode=__UpperCamelCase,  sample_weight=__UpperCamelCase,  zero_division=__UpperCamelCase,  )
							report.pop("macro avg"			)
							report.pop("weighted avg"			)
							_snake_case     	=	report.pop("micro avg"			)

							_snake_case     	=	{
							    type_name: {
							        "precision": score["precision"],
							        "recall": score["recall"],
							        "f1": score["f1-score"],
							        "number": score["support"],
							    }
							    for type_name, score in report.items()
							}
							_snake_case     	=	overall_score["precision"]
							_snake_case     	=	overall_score["recall"]
							_snake_case     	=	overall_score["f1-score"]
							_snake_case     	=	accuracy_score(y_true=__UpperCamelCase,  y_pred=__UpperCamelCase			)

							return scores











'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCamelCase							:						Tuple							     =  logging.getLogger(__name__)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






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






def 		_lowercase		(     ):



		'''simple docstring'''







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

		# 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"						,							lowercase_  )

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

		_snake_case     	=	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							)
		_snake_case     	=	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=lowercase_						,							)
		_snake_case     	=	AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path						,							from_tf=bool(".ckpt" in model_args.model_name_or_path  )						,							config=lowercase_						,							cache_dir=model_args.cache_dir						,							)

		# Get datasets
		_snake_case     	=	hasattr(model.config						,							"lang2id"  )
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir  )
		    if training_args.do_train
		    else None
		)
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							mode="dev"						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir						,							)
		    if training_args.do_eval
		    else None
		)

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

		# Initialize our Trainer
		_snake_case     	=	Trainer(
		    model=lowercase_						,							args=lowercase_						,							train_dataset=lowercase_						,							eval_dataset=lowercase_						,							data_collator=lowercase_						,							)

		# 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_master():
						tokenizer.save_pretrained(training_args.output_dir  )






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		main()


if __name__ == "__main__":
						main()





