





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


lowercase      :       Union[str, Any]									=  logging.getLogger(__name__)





class  A__    (							_lowerCamelCase	):




   """simple docstring"""



   __A   :				Any         =  '''token-classification'''

   def __init__(					self					,  lowercase)			->						Dict:







          '''simple docstring'''

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

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







          '''simple docstring'''

          return self.model(**_UpperCamelCase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Dict =				{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
          if self.config.model_type != "distilbert":
                 a__						:	Tuple =				(
                     batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
                 )  # XLM and RoBERTa don"t use token_type_ids

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

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







          '''simple docstring'''

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







          '''simple docstring'''

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

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







          '''simple docstring'''

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

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







          '''simple docstring'''

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

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

          a__						:	Optional[Any] =				{
              '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),
          }

          a__						:	Tuple =				dict(results.items())
          a__						:	Union[str, Any] =				results
          return ret, preds_list, out_label_list

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







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          a__ ,							a__ ,							a__						:	List[Any] =				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
          a__						:	Optional[Any] =				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		(					lowercase					,  lowercase)			->						str:







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









from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





