
"""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


_A						=			logging.getLogger(__name__)






@dataclass
class 	_lowercase       :
	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='NER'    ,	metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}		)
	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'}    ,	)






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


def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      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.
				lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Any          =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
		else:
				lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Dict          =   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					:      Union[str, Any]          =   import_module('tasks'      )
		try:
				lowerCamelCase					:      Union[str, Any]          =   getattr(a_,						model_args.task_type      )
				lowerCamelCase					:      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',						a_      )

		# Set seed
		set_seed(training_args.seed      )

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

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

		lowerCamelCase					:      List[Any]          =   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,						)
		lowerCamelCase					:      List[str]          =   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					:      List[Any]          =   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
		lowerCamelCase					:      Optional[int]          =   (
		    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
		)
		lowerCamelCase					:      Optional[int]          =   (
		    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]]:
				lowerCamelCase					:      Optional[int]          =   np.argmax(a_,						axis=2      )

				lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   preds.shape

				lowerCamelCase					:      List[Any]          =   [[] for _ in range(a_      )]
				lowerCamelCase					:      str          =   [[] 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:
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   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
		lowerCamelCase					:      Optional[int]          =   DataCollatorWithPadding(a_,						pad_to_multiple_of=8      ) if training_args.fpaa else None

		# Initialize our Trainer
		lowerCamelCase					:      List[str]          =   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
		lowerCamelCase					:      Tuple          =   {}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'      )

				lowerCamelCase					:      Tuple          =   trainer.evaluate()

				lowerCamelCase					:      Dict          =   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:
				lowerCamelCase					:      Tuple          =   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,						)

				lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   trainer.predict(a_      )
				lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   align_predictions(a_,						a_      )

				lowerCamelCase					:      Dict          =   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
				lowerCamelCase					:      Optional[int]          =   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_      ):





		'''simple docstring'''


		main()


if __name__ == "__main__":
					main()








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		,			UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






