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:			Optional[Any]	       =       logging.getLogger(__name__)



class       UpperCAmelCase  (						a_ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "token-classification"

       def __init__(						self     ,       __lowerCAmelCase			)  ->  str:







        if type(__lowerCAmelCase			) == dict:
         lowercase__    :   Optional[Any]						= Namespace(**__lowerCAmelCase			)
        lowercase__    :   int						= import_module('''tasks'''			)
        try:
         lowercase__    :   Dict						= getattr(__lowerCAmelCase     ,       hparams.task_type			)
         lowercase__    :   Optional[Any]						= 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__    :   Tuple						= self.token_classification_task.get_labels(hparams.labels			)
        lowercase__    :   Optional[Any]						= CrossEntropyLoss().ignore_index
        super().__init__(__lowerCAmelCase     ,       len(self.labels			)     ,       self.mode			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  str:
        return self.model(**__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   List[Any]						= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
         lowercase__    :   Tuple						= (
             batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
         )  # XLM and RoBERTa don"t use token_type_ids

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

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   List[Any]						= self.hparams
        for mode in ["train", "dev", "test"]:
         lowercase__    :   Dict						= self._feature_file(__lowerCAmelCase			)
         if os.path.exists(__lowerCAmelCase			) and not args.overwrite_cache:
          logger.info('''Loading features from cached file %s'''     ,       __lowerCAmelCase			)
          lowercase__    :   int						= torch.load(__lowerCAmelCase			)
         else:
          logger.info('''Creating features from dataset file at %s'''     ,       args.data_dir			)
          lowercase__    :   Optional[Any]						= self.token_classification_task.read_examples_from_file(args.data_dir     ,       __lowerCAmelCase			)
          lowercase__    :   Union[str, Any]						= 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = False			)  ->  DataLoader:
        lowercase__    :   Optional[int]						= self._feature_file(__lowerCAmelCase			)
        logger.info('''Loading features from cached file %s'''     ,       __lowerCAmelCase			)
        lowercase__    :   Dict						= torch.load(__lowerCAmelCase			)
        lowercase__    :   Any						= torch.tensor([f.input_ids for f in features]     ,       dtype=torch.long			)
        lowercase__    :   str						= torch.tensor([f.attention_mask for f in features]     ,       dtype=torch.long			)
        if features[0].token_type_ids is not None:
         lowercase__    :   Union[str, Any]						= torch.tensor([f.token_type_ids for f in features]     ,       dtype=torch.long			)
        else:
         lowercase__    :   str						= torch.tensor([0 for f in features]     ,       dtype=torch.long			)
         # HACK(we will not use this anymore soon)
        lowercase__    :   Dict						= torch.tensor([f.label_ids for f in features]     ,       dtype=torch.long			)
        return DataLoader(
            TensorDataset(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)     ,       batch_size=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        """Compute validation""" ""
        lowercase__    :   str						= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
         lowercase__    :   Any						= (
             batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
         )  # XLM and RoBERTa don"t use token_type_ids
        lowercase__    :   List[Any]						= self(**__lowerCAmelCase			)
        lowercase__			,							lowercase__    :   Dict						= outputs[:2]
        lowercase__    :   Optional[int]						= logits.detach().cpu().numpy()
        lowercase__    :   List[str]						= inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        lowercase__    :   Tuple						= torch.stack([x['''val_loss'''] for x in outputs]			).mean()
        lowercase__    :   int						= np.concatenate([x['''pred'''] for x in outputs]     ,       axis=0			)
        lowercase__    :   Any						= np.argmax(__lowerCAmelCase     ,       axis=2			)
        lowercase__    :   Union[str, Any]						= np.concatenate([x['''target'''] for x in outputs]     ,       axis=0			)

        lowercase__    :   Optional[int]						= dict(enumerate(self.labels			)			)
        lowercase__    :   Dict						= [[] for _ in range(out_label_ids.shape[0]			)]
        lowercase__    :   Any						= [[] 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__    :   List[str]						= {
            '''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						= dict(results.items()			)
        lowercase__    :   List[str]						= results
        return ret, preds_list, out_label_list

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__			,							lowercase__			,							lowercase__    :   Any						= self._eval_end(__lowerCAmelCase			)
        lowercase__    :   str						= ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__			,							lowercase__			,							lowercase__    :   Union[str, Any]						= 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__    :   Dict						= 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 _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Dict:
        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=128     ,       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__":
  __a:			List[str]	       =       argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __a:			List[Any]	       =       NERTransformer.add_model_specific_args(parser, os.getcwd())
  __a:			List[Any]	       =       parser.parse_args()
  __a:			Tuple	       =       NERTransformer(args)
  __a:			List[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
    __a:			List[str]	       =       sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __a:			List[Any]	       =       model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)


'''simple docstring'''

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

import numpy as np
import torch

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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCAmelCase					) / alpha_bar_fn(UpperCAmelCase					)		,					UpperCAmelCase					)					)
 return torch.tensor(UpperCAmelCase		,					dtype=torch.floataa					)





class       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 1 - alpha_prod_t / alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 1 - beta

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= model_output
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             ''' for the UnCLIPScheduler.'''			)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -self.config.clip_sample_range     ,       self.config.clip_sample_range			)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (0.5 * variance).exp()
         else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              ''' for the UnCLIPScheduler.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

        lowercase__    :   int						= (1 - alphas_cumprod[timesteps]) ** 0.5
        lowercase__    :   List[Any]						= sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   int						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

