






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       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Optional[Any]								=			"token-classification"



       def __init__(				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    **a__     )			->   Optional[Any]:






            '''simple docstring'''





            return self.model(**a__     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Any:






            '''simple docstring'''





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

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



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = False     )			->   DataLoader:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    a__     )			->   Optional[Any]:






            '''simple docstring'''





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

            __snake_case      :str 							=    dict(enumerate(self.labels     )     )
            __snake_case      :Optional[int] 							=    [[] for _ in range(out_label_ids.shape[0]     )]
            __snake_case      :str 							=    [[] 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      :List[Any] 							=    {
                """val_loss""": val_loss_mean,
                """accuracy_score""": accuracy_score(a__      ,    a__     ),
                """precision""": precision_score(a__      ,    a__     ),
                """recall""": recall_score(a__      ,    a__     ),
                """f1""": fa_score(a__      ,    a__     ),
            }

            __snake_case      :str 							=    dict(results.items()     )
            __snake_case      :str 							=    results
            return ret, preds_list, out_label_list



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case							,			__snake_case							,			__snake_case      :int 							=    self._eval_end(a__     )
            __snake_case      :str 							=    ret["""log"""]
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            __snake_case							,			__snake_case							,			__snake_case      :Dict 							=    self._eval_end(a__     )

            # Converting to the dict required by pl
            # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
            # pytorch_lightning/trainer/logging.py#L139
            __snake_case      :Optional[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    (				a__      ,    a__     )			->   Any:






            '''simple docstring'''





            BaseTransformer.add_model_specific_args(a__      ,    a__     )
            parser.add_argument(
                """--task_type"""      ,    default="""NER"""      ,    type=a__      ,    help="""Task type to fine tune in training (e.g. NER, POS, etc)"""     )
            parser.add_argument(
                """--max_seq_length"""      ,    default=1_28      ,    type=a__      ,    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=a__      ,    help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""      ,    )
            parser.add_argument(
                """--gpus"""      ,    default=0      ,    type=a__      ,    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)










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


