



"""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


UpperCAmelCase						:		Tuple			= logging.getLogger(__name__)


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   "token-classification"

    def __init__(				self			:				Tuple ,  lowerCAmelCase_			:				Tuple):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  **lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      return self.model(**lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

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

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







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







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				int):







      """simple docstring"""







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

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

      lowercase_								= {
          """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_),
      }

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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      lowercase_					,    lowercase_					,    lowercase_								= 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
      lowercase_								= 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	(				lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      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=1_2_8 ,  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__":
       UpperCAmelCase						:		int			= argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       UpperCAmelCase						:		Union[str, Any]			= NERTransformer.add_model_specific_args(parser, os.getcwd())
       UpperCAmelCase						:		Dict			= parser.parse_args()
       UpperCAmelCase						:		int			= NERTransformer(args)
       UpperCAmelCase						:		Any			= 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
              UpperCAmelCase						:		Optional[int]			= sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
              UpperCAmelCase						:		List[Any]			= model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowercase_								= single_char_strings[ch]
      lowercase_								= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase							)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

  # two len string
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= int(__lowerCAmelCase							) / all_sum
        my_sec_sum += prob * math.loga(__lowerCAmelCase							)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0 ,  len(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
       main()

