






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	:							Tuple				=logging.getLogger(__name__)





class 			__a  (    UpperCAmelCase__       ):
 _lowerCAmelCase       :       Union[str, Any]        = '''token-classification'''







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


      '''simple docstring'''






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







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


      '''simple docstring'''






      return self.model(**__lowercase						)







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


      '''simple docstring'''






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

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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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


      '''simple docstring'''






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







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


      '''simple docstring'''






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







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


      '''simple docstring'''






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

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

      UpperCamelCase__			:   Tuple       				=  {
          "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						),
      }

      UpperCamelCase__			:   Union[str, Any]       				=  dict(results.items()						)
      UpperCamelCase__			:   Union[str, Any]       				=  results
      return ret, preds_list, out_label_list







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


      '''simple docstring'''






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







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


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  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
      UpperCamelCase__			:   int       				=  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					(		SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   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__":
 lowerCamelCase	:							List[str]				=argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 lowerCamelCase	:							Optional[int]				=NERTransformer.add_model_specific_args(parser, os.getcwd())
 lowerCamelCase	:							Union[str, Any]				=parser.parse_args()
 lowerCamelCase	:							int				=NERTransformer(args)
 lowerCamelCase	:							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
  lowerCamelCase	:							int				=sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
  lowerCamelCase	:							int				=model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)





import functools


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							) or not all(isinstance(_A     ,	_A							) for day in days							):
		raise ValueError("The parameter days should be a list of integers"							)

	if len(_A							) != 3 or not all(isinstance(_A     ,	_A							) for cost in costs							):
		raise ValueError("The parameter costs should be a list of three integers"							)

	if len(_A							) == 0:
		return 0

	if min(_A							) <= 0:
		raise ValueError("All days elements should be greater than 0"							)

	if max(_A							) >= 366:
		raise ValueError("All days elements should be less than 366"							)

	snake_case_		    =     set(_A							)

	@functools.cache
	def dynamic_programming(_A							) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1							)

		return min(
		    costs[0] + dynamic_programming(index + 1							)     ,	costs[1] + dynamic_programming(index + 7							)     ,	costs[2] + dynamic_programming(index + 30							)     ,	)

	return dynamic_programming(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
