


"""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


__snake_case       =       logging.getLogger(__name__)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    '''token-classification'''







   def __init__(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       if type(UpperCamelCase__		) == dict:
           snake_case       : List[Any]    =       Namespace(**UpperCamelCase__		)
       snake_case       : Tuple    =       import_module("tasks"		)
       try:
           snake_case       : Tuple    =       getattr(UpperCamelCase__		,      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       : Optional[Any]    =       self.token_classification_task.get_labels(hparams.labels		)
       snake_case       : Tuple    =       CrossEntropyLoss().ignore_index
       super().__init__(UpperCamelCase__		,      len(self.labels		)		,      self.mode		)







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       return self.model(**UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Dict    =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
           snake_case       : Optional[Any]    =       (
               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(**UpperCamelCase__		)
       snake_case       : Union[str, Any]    =       outputs[0]
       # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss}







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

       '''simple docstring'''




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

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       """Compute validation""" ""
       snake_case       : Optional[int]    =       {"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       : List[str]    =       self(**UpperCamelCase__		)
       snake_case   ,snake_case       : Dict    =       outputs[:2]
       snake_case       : Union[str, Any]    =       logits.detach().cpu().numpy()
       snake_case       : Union[str, Any]    =       inputs["labels"].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




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

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

       snake_case       : List[str]    =       {
           "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__		),
       }

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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case   ,snake_case   ,snake_case       : int    =       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
       snake_case       : 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 					lowerCamelCase						(      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       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__":
  __snake_case       =       argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __snake_case       =       NERTransformer.add_model_specific_args(parser, os.getcwd())
  __snake_case       =       parser.parse_args()
  __snake_case       =       NERTransformer(args)
  __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
    __snake_case       =       sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __snake_case       =       model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)









"""simple docstring"""



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case   ,snake_case       : List[str]    =       image.size
    snake_case   ,snake_case       : int    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case       : Any    =       image.resize((w, h)		,				resample=PIL_INTERPOLATION["lanczos"]					)
    snake_case       : Dict    =       np.array(lowercase					).astype(np.floataa					) / 255.0
    snake_case       : List[str]    =       image[None].transpose(0		,				3		,				1		,				2					)
    snake_case       : List[Any]    =       torch.from_numpy(lowercase					)
    return 2.0 * image - 1.0







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       self.register_modules(vqvae=UpperCamelCase__		,      unet=UpperCamelCase__		,      scheduler=UpperCamelCase__		)







   @torch.no_grad()
   def __call__(      self		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 100		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "pil"		,      UpperCamelCase__ = True		,      )      ->						Union[Tuple, ImagePipelineOutput]:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       image.shape[0]
       else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'		)

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case       : Any    =       (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case       : List[str]    =       next(self.unet.parameters()		).dtype

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case       : Optional[int]    =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       snake_case       : Optional[int]    =       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       snake_case       : Union[str, Any]    =       {}
       if accepts_eta:
           snake_case       : List[str]    =       eta

       for t in self.progress_bar(UpperCamelCase__		):
           # concat latents and low resolution image in the channel dimension.
           snake_case       : int    =       torch.cat([latents, image]		,      dim=1		)
           snake_case       : List[Any]    =       self.scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)
           # predict the noise residual
           snake_case       : int    =       self.unet(UpperCamelCase__		,      UpperCamelCase__		).sample
           # compute the previous noisy sample x_t -> x_t-1
           snake_case       : List[Any]    =       self.scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		).prev_sample

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





