




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 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   '''token-classification'''






						def __init__(						self							:     List[str]       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Dict       ,	**__lowercase							:     Dict						):






							"""simple docstring"""






							return self.model(**__lowercase						)






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     str       ,	__lowercase							:     int						):






							"""simple docstring"""






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

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






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     int       ,	__lowercase							:     int       ,	__lowercase							:     int       ,	__lowercase							:     bool = False						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     Dict						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     int						):






							"""simple docstring"""






							snake_case_		    =     torch.stack([x["val_loss"] for x in outputs]						).mean()
							snake_case_		    =     np.concatenate([x["pred"] for x in outputs]       ,	axis=0						)
							snake_case_		    =     np.argmax(__lowercase       ,	axis=2						)
							snake_case_		    =     np.concatenate([x["target"] for x in outputs]       ,	axis=0						)

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

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

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






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     Tuple						):






							"""simple docstring"""






							snake_case_    ,      snake_case_    ,      snake_case_		    =     self._eval_end(__lowercase						)
							snake_case_		    =     ret["log"]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							snake_case_    ,      snake_case_    ,      snake_case_		    =     self._eval_end(__lowercase						)

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							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        snake_case__						(						__lowercase							:     Any       ,	__lowercase							:     List[Any]						):






							"""simple docstring"""






							BaseTransformer.add_model_specific_args(__lowercase       ,	__lowercase						)
							parser.add_argument(
							    "--task_type"       ,	default="NER"       ,	type=__lowercase       ,	help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
							parser.add_argument(
							    "--max_seq_length"       ,	default=1_28       ,	type=__lowercase       ,	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=__lowercase       ,	help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."       ,	)
							parser.add_argument(
							    "--gpus"       ,	default=0       ,	type=__lowercase       ,	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__				:     List[str] 		=						argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		lowercase__				:     Optional[int] 		=						NERTransformer.add_model_specific_args(parser, os.getcwd())
		lowercase__				:     Union[str, Any] 		=						parser.parse_args()
		lowercase__				:     int 		=						NERTransformer(args)
		lowercase__				:     Optional[int] 		=						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__				:     int 		=						sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
				lowercase__				:     int 		=						model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)






# 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 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     ArgumentParser("Accelerate CLI tool"     ,	usage="accelerate <command> [<args>]"     ,	allow_abbrev=_A							)
	snake_case_		    =     parser.add_subparsers(help="accelerate command helpers"							)

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

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

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

	# Run
	args.func(_A							)


if __name__ == "__main__":
		main()
