




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


@dataclass
class   UpperCAmelCase_    :
		UpperCamelCase							=field(
		    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}		)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}   ,					)
		UpperCamelCase							=field(
		    default=1_28   ,					metadata={
		        "help": (
		            "The maximum total input sequence length after tokenization. Sequences longer "
		            "than this will be truncated, sequences shorter will be padded."
		        )
		    }   ,					)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}		)







def      __UpperCAmelCase     ( ):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

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

			__lowercase			:  int								=       import_module('''tasks'''	)
			try:
						__lowercase			:  List[Any]								=       getattr(__UpperCamelCase	,				model_args.task_type	)
						__lowercase			:  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'''	,				__UpperCamelCase	)

			# Set seed
			set_seed(training_args.seed	)

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

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

			__lowercase			:  int								=       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	,				)
			__lowercase			:  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	,				)
			__lowercase			:  Dict								=       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
			__lowercase			:  List[str]								=       (
			    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
			)
			__lowercase			:  List[str]								=       (
			    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(__UpperCamelCase	,				__UpperCamelCase	) -> Tuple[List[int], List[int]]:
						__lowercase			:  int								=       np.argmax(__UpperCamelCase	,				axis=2	)

						__lowercase			,__lowercase			:  Optional[Any]								=       preds.shape

						__lowercase			:  Any								=       [[] for _ in range(__UpperCamelCase	)]
						__lowercase			:  List[str]								=       [[] 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(__UpperCamelCase	) -> Dict:
						__lowercase			,__lowercase			:  str								=       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
			__lowercase			:  Tuple								=       DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8	) if training_args.fpaa else None

			# Initialize our Trainer
			__lowercase			:  Tuple								=       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
			__lowercase			:  Union[str, Any]								=       {}
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''	)

						__lowercase			:  List[Any]								=       trainer.evaluate()

						__lowercase			:  Optional[Any]								=       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:
						__lowercase			:  List[str]								=       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	,				)

						__lowercase			,__lowercase			,__lowercase			:  Any								=       trainer.predict(__UpperCamelCase	)
						__lowercase			,__lowercase			:  int								=       align_predictions(__UpperCamelCase	,				__UpperCamelCase	)

						__lowercase			:  Tuple								=       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
						__lowercase			:  Optional[int]								=       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      __UpperCAmelCase     ( __UpperCamelCase	):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
			main()






import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)