


"""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


_lowerCamelCase     =  logging.getLogger(__name__)


class 			lowerCamelCase_		(			__UpperCAmelCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      int      							=					"token-classification"



				def __init__(						self       ,		UpperCAmelCase__     ):







								if type(lowerCAmelCase_     ) == dict:
												SCREAMING_SNAKE_CASE__     							=				Namespace(**lowerCAmelCase_     )
								SCREAMING_SNAKE_CASE__     							=				import_module("tasks"     )
								try:
												SCREAMING_SNAKE_CASE__     							=				getattr(lowerCAmelCase_       ,		hparams.task_type     )
												SCREAMING_SNAKE_CASE__     							=				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__()}'''     )
								SCREAMING_SNAKE_CASE__     							=				self.token_classification_task.get_labels(hparams.labels     )
								SCREAMING_SNAKE_CASE__     							=				CrossEntropyLoss().ignore_index
								super().__init__(lowerCAmelCase_       ,		len(self.labels     )       ,		self.mode     )



				def   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
								return self.model(**lowerCAmelCase_     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
								if self.config.model_type != "distilbert":
												SCREAMING_SNAKE_CASE__     							=				(
												    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
												)  # XLM and RoBERTa don"t use token_type_ids

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.hparams
								for mode in ["train", "dev", "test"]:
												SCREAMING_SNAKE_CASE__     							=				self._feature_file(lowerCAmelCase_     )
												if os.path.exists(lowerCAmelCase_     ) and not args.overwrite_cache:
																logger.info("Loading features from cached file %s"       ,		lowerCAmelCase_     )
																SCREAMING_SNAKE_CASE__     							=				torch.load(lowerCAmelCase_     )
												else:
																logger.info("Creating features from dataset file at %s"       ,		args.data_dir     )
																SCREAMING_SNAKE_CASE__     							=				self.token_classification_task.read_examples_from_file(args.data_dir       ,		lowerCAmelCase_     )
																SCREAMING_SNAKE_CASE__     							=				self.token_classification_task.convert_examples_to_features(
																    lowerCAmelCase_       ,		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=lowerCAmelCase_       ,		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"       ,		lowerCAmelCase_     )
																torch.save(lowerCAmelCase_       ,		lowerCAmelCase_     )



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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								"""Compute validation""" ""
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
								if self.config.model_type != "distilbert":
												SCREAMING_SNAKE_CASE__     							=				(
												    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
												)  # XLM and RoBERTa don"t use token_type_ids
								SCREAMING_SNAKE_CASE__     							=				self(**lowerCAmelCase_     )
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				outputs[:2]
								SCREAMING_SNAKE_CASE__     							=				logits.detach().cpu().numpy()
								SCREAMING_SNAKE_CASE__     							=				inputs["labels"].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				torch.stack([x["val_loss"] for x in outputs]     ).mean()
								SCREAMING_SNAKE_CASE__     							=				np.concatenate([x["pred"] for x in outputs]       ,		axis=0     )
								SCREAMING_SNAKE_CASE__     							=				np.argmax(lowerCAmelCase_       ,		axis=2     )
								SCREAMING_SNAKE_CASE__     							=				np.concatenate([x["target"] for x in outputs]       ,		axis=0     )

								SCREAMING_SNAKE_CASE__     							=				dict(enumerate(self.labels     )     )
								SCREAMING_SNAKE_CASE__     							=				[[] for _ in range(out_label_ids.shape[0]     )]
								SCREAMING_SNAKE_CASE__     							=				[[] 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]]     )

								SCREAMING_SNAKE_CASE__     							=				{
								    "val_loss": val_loss_mean,
								    "accuracy_score": accuracy_score(lowerCAmelCase_       ,		lowerCAmelCase_     ),
								    "precision": precision_score(lowerCAmelCase_       ,		lowerCAmelCase_     ),
								    "recall": recall_score(lowerCAmelCase_       ,		lowerCAmelCase_     ),
								    "f1": fa_score(lowerCAmelCase_       ,		lowerCAmelCase_     ),
								}

								SCREAMING_SNAKE_CASE__     							=				dict(results.items()     )
								SCREAMING_SNAKE_CASE__     							=				results
								return ret, preds_list, out_label_list



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self._eval_end(lowerCAmelCase_     )
								SCREAMING_SNAKE_CASE__     							=				ret["log"]
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self._eval_end(lowerCAmelCase_     )

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						UpperCAmelCase__       ,		UpperCAmelCase__     ):
								BaseTransformer.add_model_specific_args(lowerCAmelCase_       ,		lowerCAmelCase_     )
								parser.add_argument(
								    "--task_type"       ,		default="NER"       ,		type=lowerCAmelCase_       ,		help="Task type to fine tune in training (e.g. NER, POS, etc)"     )
								parser.add_argument(
								    "--max_seq_length"       ,		default=128       ,		type=lowerCAmelCase_       ,		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=lowerCAmelCase_       ,		help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."       ,		)
								parser.add_argument(
								    "--gpus"       ,		default=0       ,		type=lowerCAmelCase_       ,		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__":
	_lowerCamelCase     =  argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	_lowerCamelCase     =  NERTransformer.add_model_specific_args(parser, os.getcwd())
	_lowerCamelCase     =  parser.parse_args()
	_lowerCamelCase     =  NERTransformer(args)
	_lowerCamelCase     =  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
		_lowerCamelCase     =  sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
		_lowerCamelCase     =  model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
