"""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


__A						       =      logging.getLogger(__name__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """token-classification"""

				def __init__(     self							:    int					,  UpperCamelCase__							:    Tuple)->       Tuple:



						'''simple docstring'''


						if type(UpperCamelCase__) == dict:
								__lowerCAmelCase:	Optional[Any]   				=      Namespace(**UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      import_module("tasks")
						try:
								__lowerCAmelCase:	Optional[int]   				=      getattr(UpperCamelCase__					,  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:	List[str]   				=      self.token_classification_task.get_labels(hparams.labels)
						__lowerCAmelCase:	Any   				=      CrossEntropyLoss().ignore_index
						super().__init__(UpperCamelCase__					,  len(self.labels)					,  self.mode)

				def 							lowercase_					(     self							:    Union[str, Any]					,  **UpperCamelCase__							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						return self.model(**UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


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

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

				def 							lowercase_					(     self							:    str)->       Any:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    bool = False)->       DataLoader:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self._feature_file(UpperCamelCase__)
						logger.info("Loading features from cached file %s"					,  UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      torch.load(UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      torch.tensor([f.input_ids for f in features]					,  dtype=torch.long)
						__lowerCAmelCase:	int   				=      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:	str   				=      torch.tensor([0 for f in features]					,  dtype=torch.long)
								# HACK(we will not use this anymore soon)
						__lowerCAmelCase:	List[str]   				=      torch.tensor([f.label_ids for f in features]					,  dtype=torch.long)
						return DataLoader(
						    TensorDataset(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)					,  batch_size=UpperCamelCase__)

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int])->       Union[str, Any]:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    int)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      torch.stack([x["val_loss"] for x in outputs]).mean()
						__lowerCAmelCase:	Dict   				=      np.concatenate([x["pred"] for x in outputs]					,  axis=0)
						__lowerCAmelCase:	List[Any]   				=      np.argmax(UpperCamelCase__					,  axis=2)
						__lowerCAmelCase:	List[str]   				=      np.concatenate([x["target"] for x in outputs]					,  axis=0)

						__lowerCAmelCase:	Tuple   				=      dict(enumerate(self.labels))
						__lowerCAmelCase:	Union[str, Any]   				=      [[] 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:	Tuple   				=      {
						    "val_loss": val_loss_mean,
						    "accuracy_score": accuracy_score(UpperCamelCase__					,  UpperCamelCase__),
						    "precision": precision_score(UpperCamelCase__					,  UpperCamelCase__),
						    "recall": recall_score(UpperCamelCase__					,  UpperCamelCase__),
						    "f1": fa_score(UpperCamelCase__					,  UpperCamelCase__),
						}

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

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Optional[Any])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self._eval_end(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      ret["log"]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self._eval_end(UpperCamelCase__)

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						__lowerCAmelCase:	List[Any]   				=      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 							lowercase_					(     UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Optional[int])->       Tuple:



						'''simple docstring'''


						BaseTransformer.add_model_specific_args(UpperCamelCase__					,  UpperCamelCase__)
						parser.add_argument(
						    "--task_type"					,  default="NER"					,  type=UpperCamelCase__					,  help="Task type to fine tune in training (e.g. NER, POS, etc)")
						parser.add_argument(
						    "--max_seq_length"					,  default=1_2_8					,  type=UpperCamelCase__					,  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=UpperCamelCase__					,  help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."					,  )
						parser.add_argument(
						    "--gpus"					,  default=0					,  type=UpperCamelCase__					,  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__":
			__A						       =      argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			__A						       =      NERTransformer.add_model_specific_args(parser, os.getcwd())
			__A						       =      parser.parse_args()
			__A						       =      NERTransformer(args)
			__A						       =      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
						__A						       =      sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
						__A						       =      model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


