
"""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


SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			'''token-classification'''






       def __init__(  self :							int       ,      UpperCAmelCase_ :							str			)->  Tuple:







              """simple docstring"""
              if type(UpperCAmelCase_			) == dict:
                     UpperCamelCase					 =		Namespace(**UpperCAmelCase_			)
              UpperCamelCase					 =		import_module("tasks"			)
              try:
                     UpperCamelCase					 =		getattr(UpperCAmelCase_       ,      hparams.task_type			)
                     UpperCamelCase					 =		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					 =		self.token_classification_task.get_labels(hparams.labels			)
              UpperCamelCase					 =		CrossEntropyLoss().ignore_index
              super().__init__(UpperCAmelCase_       ,      len(self.labels			)       ,      self.mode			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      **UpperCAmelCase_ :							List[Any]			)->  List[str]:







              """simple docstring"""
              return self.model(**UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							Union[str, Any]			)->  str:







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

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






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







              """simple docstring"""
              UpperCamelCase					 =		self.hparams
              for mode in ["train", "dev", "test"]:
                     UpperCamelCase					 =		self._feature_file(UpperCAmelCase_			)
                     if os.path.exists(UpperCAmelCase_			) and not args.overwrite_cache:
                            logger.info("Loading features from cached file %s"       ,      UpperCAmelCase_			)
                            UpperCamelCase					 =		torch.load(UpperCAmelCase_			)
                     else:
                            logger.info("Creating features from dataset file at %s"       ,      args.data_dir			)
                            UpperCamelCase					 =		self.token_classification_task.read_examples_from_file(args.data_dir       ,      UpperCAmelCase_			)
                            UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							bool = False			)->  DataLoader:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Any			)->  Dict:







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

              UpperCamelCase					 =		dict(enumerate(self.labels			)			)
              UpperCamelCase					 =		[[] for _ in range(out_label_ids.shape[0]			)]
              UpperCamelCase					 =		[[] 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					 =		{
                  "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_			),
              }

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Tuple:







              """simple docstring"""
              # when stable
              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		self._eval_end(UpperCAmelCase_			)
              UpperCamelCase					 =		ret["log"]
              return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							Optional[Any]			)->  Tuple:







              """simple docstring"""
              # updating to test_epoch_end instead of deprecated test_end
              UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		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
              UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              # Add NER specific options
              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=128       ,      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__":
      SCREAMING_SNAKE_CASE      =     argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      SCREAMING_SNAKE_CASE      =     NERTransformer.add_model_specific_args(parser, os.getcwd())
      SCREAMING_SNAKE_CASE      =     parser.parse_args()
      SCREAMING_SNAKE_CASE      =     NERTransformer(args)
      SCREAMING_SNAKE_CASE      =     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
            SCREAMING_SNAKE_CASE      =     sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
            SCREAMING_SNAKE_CASE      =     model.load_from_checkpoint(checkpoints[-1])
            trainer.test(model)







"""simple docstring"""


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			(EulerDiscreteScheduler,)
       UpperCamelCase_		:					Dict   =			10






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      **UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		{
                  "num_train_timesteps": 1_100,
                  "beta_start": 0.0001,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**UpperCAmelCase_			)
              return config






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Any:







              """simple docstring"""
              for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,      [0.0002, 0.002, 0.02]			):
                     self.check_over_configs(beta_start=UpperCAmelCase_       ,      beta_end=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Dict:







              """simple docstring"""
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config(prediction_type="v_prediction"			)
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 0.0002			) < 1e-2
              assert abs(result_mean.item() - 2.2_676e-06			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_       ,      use_karras_sigmas=UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 124.52299499511719			) < 1e-2
              assert abs(result_mean.item() - 0.16213932633399963			) < 1e-3





