
'''simple docstring'''


import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__lowercase							:   Tuple					=     logging.getLogger(__name__)

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		List[str]	      =			"token-classification"

			def __init__(self			,   A      ):
								if type(A      ) == dict:
													lowerCamelCase_					:      Dict     =		Namespace(**A      )
								lowerCamelCase_					:      List[Any]     =		import_module('''tasks'''      )
								try:
													lowerCamelCase_					:      Dict     =		getattr(A			,   hparams.task_type      )
													lowerCamelCase_					:      TokenClassificationTask     =		token_classification_task_clazz()
								except AttributeError:
													raise ValueError(
													    F"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
													    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""      )
								lowerCamelCase_					:      str     =		self.token_classification_task.get_labels(hparams.labels      )
								lowerCamelCase_					:      str     =		CrossEntropyLoss().ignore_index
								super().__init__(A			,   len(self.labels      )			,   self.mode      )

			def        UpperCAmelCase__ (self			,   **A      ):
								return self.model(**A      )

			def        UpperCAmelCase__ (self			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
								if self.config.model_type != "distilbert":
													lowerCamelCase_					:      Any     =		(
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids

								lowerCamelCase_					:      Dict     =		self(**A      )
								lowerCamelCase_					:      Dict     =		outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.hparams
								for mode in ["train", "dev", "test"]:
													lowerCamelCase_					:      Optional[int]     =		self._feature_file(A      )
													if os.path.exists(A      ) and not args.overwrite_cache:
																		logger.info('''Loading features from cached file %s'''			,   A      )
																		lowerCamelCase_					:      List[Any]     =		torch.load(A      )
													else:
																		logger.info('''Creating features from dataset file at %s'''			,   args.data_dir      )
																		lowerCamelCase_					:      Optional[Any]     =		self.token_classification_task.read_examples_from_file(args.data_dir			,   A      )
																		lowerCamelCase_					:      int     =		self.token_classification_task.convert_examples_to_features(
																		    A			,   self.labels			,   args.max_seq_length			,   self.tokenizer			,   cls_token_at_end=bool(self.config.model_type in ['''xlnet''']      )			,   cls_token=self.tokenizer.cls_token			,   cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0			,   sep_token=self.tokenizer.sep_token			,   sep_token_extra=A			,   pad_on_left=bool(self.config.model_type in ['''xlnet''']      )			,   pad_token=self.tokenizer.pad_token_id			,   pad_token_segment_id=self.tokenizer.pad_token_type_id			,   pad_token_label_id=self.pad_token_label_id			,   )
																		logger.info('''Saving features into cached file %s'''			,   A      )
																		torch.save(A			,   A      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A = False      ):
								lowerCamelCase_					:      List[Any]     =		self._feature_file(A      )
								logger.info('''Loading features from cached file %s'''			,   A      )
								lowerCamelCase_					:      Optional[Any]     =		torch.load(A      )
								lowerCamelCase_					:      List[Any]     =		torch.tensor([f.input_ids for f in features]			,   dtype=torch.long      )
								lowerCamelCase_					:      Dict     =		torch.tensor([f.attention_mask for f in features]			,   dtype=torch.long      )
								if features[0].token_type_ids is not None:
													lowerCamelCase_					:      int     =		torch.tensor([f.token_type_ids for f in features]			,   dtype=torch.long      )
								else:
													lowerCamelCase_					:      Optional[int]     =		torch.tensor([0 for f in features]			,   dtype=torch.long      )
													# HACK(we will not use this anymore soon)
								lowerCamelCase_					:      Optional[Any]     =		torch.tensor([f.label_ids for f in features]			,   dtype=torch.long      )
								return DataLoader(
								    TensorDataset(A			,   A			,   A			,   A      )			,   batch_size=A      )

			def        UpperCAmelCase__ (self			,   A			,   A      ):
								"""Compute validation""" ""
								lowerCamelCase_					:      Union[str, Any]     =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
								if self.config.model_type != "distilbert":
													lowerCamelCase_					:      Tuple     =		(
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids
								lowerCamelCase_					:      Optional[int]     =		self(**A      )
								lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		outputs[:2]
								lowerCamelCase_					:      Tuple     =		logits.detach().cpu().numpy()
								lowerCamelCase_					:      str     =		inputs['''labels'''].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      int     =		torch.stack([x['''val_loss'''] for x in outputs]      ).mean()
								lowerCamelCase_					:      Tuple     =		np.concatenate([x['''pred'''] for x in outputs]			,   axis=0      )
								lowerCamelCase_					:      Optional[Any]     =		np.argmax(A			,   axis=2      )
								lowerCamelCase_					:      Optional[int]     =		np.concatenate([x['''target'''] for x in outputs]			,   axis=0      )

								lowerCamelCase_					:      str     =		dict(enumerate(self.labels      )      )
								lowerCamelCase_					:      int     =		[[] for _ in range(out_label_ids.shape[0]      )]
								lowerCamelCase_					:      int     =		[[] for _ in range(out_label_ids.shape[0]      )]

								for i in range(out_label_ids.shape[0]      ):
													for j in range(out_label_ids.shape[1]      ):
																		if out_label_ids[i, j] != self.pad_token_label_id:
																							out_label_list[i].append(label_map[out_label_ids[i][j]]      )
																							preds_list[i].append(label_map[preds[i][j]]      )

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''val_loss''': val_loss_mean,
								    '''accuracy_score''': accuracy_score(A			,   A      ),
								    '''precision''': precision_score(A			,   A      ),
								    '''recall''': recall_score(A			,   A      ),
								    '''f1''': fa_score(A			,   A      ),
								}

								lowerCamelCase_					:      List[str]     =		dict(results.items()      )
								lowerCamelCase_					:      Tuple     =		results
								return ret, preds_list, out_label_list

			def        UpperCAmelCase__ (self			,   A      ):
								# when stable
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		self._eval_end(A      )
								lowerCamelCase_					:      Optional[Any]     =		ret['''log''']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def        UpperCAmelCase__ (self			,   A      ):
								# updating to test_epoch_end instead of deprecated test_end
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		self._eval_end(A      )

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								lowerCamelCase_					:      List[str]     =		ret['''log''']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






			@staticmethod
			def        UpperCAmelCase__ (A			,   A      ):
								# Add NER specific options
								BaseTransformer.add_model_specific_args(A			,   A      )
								parser.add_argument(
								    '''--task_type'''			,   default='''NER'''			,   type=A			,   help='''Task type to fine tune in training (e.g. NER, POS, etc)'''      )
								parser.add_argument(
								    '''--max_seq_length'''			,   default=1_2_8			,   type=A			,   help=(
								        '''The maximum total input sequence length after tokenization. Sequences longer '''
								        '''than this will be truncated, sequences shorter will be padded.'''
								    )			,   )

								parser.add_argument(
								    '''--labels'''			,   default=''''''			,   type=A			,   help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''			,   )
								parser.add_argument(
								    '''--gpus'''			,   default=0			,   type=A			,   help='''The number of GPUs allocated for this, it is by default 0 meaning none'''			,   )

								parser.add_argument(
								    '''--overwrite_cache'''			,   action='''store_true'''			,   help='''Overwrite the cached training and evaluation sets'''      )

								return parser


if __name__ == "__main__":
					__lowercase							:   Dict					=     argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					__lowercase							:   Optional[Any]					=     NERTransformer.add_model_specific_args(parser, os.getcwd())
					__lowercase							:   Optional[int]					=     parser.parse_args()
					__lowercase							:   Optional[Any]					=     NERTransformer(args)
					__lowercase							:   str					=     generic_train(model, args)

					if args.do_predict:
										# See https://github.com/huggingface/transformers/issues/3159
										# pl use this default format to create a checkpoint:
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
										# /pytorch_lightning/callbacks/model_checkpoint.py#L322
										__lowercase							:   Any					=     sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
										__lowercase							:   Optional[int]					=     model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


