





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase_					     =	logging.getLogger(__name__)
@dataclass
class        SCREAMING_SNAKE_CASE     :

   '''simple docstring'''





   _UpperCamelCase					:	Union[str, Any]				      = field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
   _UpperCamelCase					:	List[Any]				      = field(
       default=UpperCAmelCase__    ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
   _UpperCamelCase					:	int				      = field(
       default='NER'    ,			metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}   )
   _UpperCamelCase					:	int				      = field(
       default=UpperCAmelCase__    ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
   _UpperCamelCase					:	Optional[int]				      = field(default=UpperCAmelCase__    ,			metadata={'help': 'Set this flag to use fast tokenization.'}   )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   _UpperCamelCase					:	List[Any]				      = field(
       default=UpperCAmelCase__    ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,			)






@dataclass
class        SCREAMING_SNAKE_CASE     :

   '''simple docstring'''





   _UpperCamelCase					:	List[str]				      = field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}   )
   _UpperCamelCase					:	Dict				      = field(
       default=UpperCAmelCase__    ,			metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    ,			)
   _UpperCamelCase					:	List[Any]				      = field(
       default=1_28    ,			metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }    ,			)
   _UpperCamelCase					:	List[Any]				      = field(
       default=UpperCAmelCase__    ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )






def     __UpperCamelCase						()       ->     int:
  lowercase__			    =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
  if len(sys.argv	) == 2 and sys.argv[1].endswith('.json'	):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowercase__    ,					lowercase__    ,					lowercase__			    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
  else:
    lowercase__    ,					lowercase__    ,					lowercase__			    =						parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir	)
      and os.listdir(training_args.output_dir	)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
        ' --overwrite_output_dir to overcome.'	)

  lowercase__			    =						import_module('tasks'	)
  try:
    lowercase__			    =						getattr(UpperCAmelCase__	,		model_args.task_type	)
    lowercase__			    =						token_classification_task_clazz()
  except AttributeError:
    raise ValueError(
        F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
        F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""	)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,		datefmt='%m/%d/%Y %H:%M:%S'	,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,		)
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'	,		training_args.local_rank	,		training_args.device	,		training_args.n_gpu	,		bool(training_args.local_rank != -1	)	,		training_args.fpaa	,		)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank	):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'	,		UpperCAmelCase__	)

  # Set seed
  set_seed(training_args.seed	)

  # Prepare CONLL-2003 task
  lowercase__			    =						token_classification_task.get_labels(data_args.labels	)
  lowercase__			    =						dict(enumerate(UpperCAmelCase__	)	)
  lowercase__			    =						len(UpperCAmelCase__	)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowercase__			    =						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path	,		num_labels=UpperCAmelCase__	,		idalabel=UpperCAmelCase__	,		labelaid={label: i for i, label in enumerate(UpperCAmelCase__	)}	,		cache_dir=model_args.cache_dir	,		)
  lowercase__			    =						AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,		cache_dir=model_args.cache_dir	,		use_fast=model_args.use_fast	,		)
  lowercase__			    =						AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path	,		from_tf=bool('.ckpt' in model_args.model_name_or_path	)	,		config=UpperCAmelCase__	,		cache_dir=model_args.cache_dir	,		)

  # Get datasets
  lowercase__			    =						(
      TokenClassificationDataset(
          token_classification_task=UpperCAmelCase__	,		data_dir=data_args.data_dir	,		tokenizer=UpperCAmelCase__	,		labels=UpperCAmelCase__	,		model_type=config.model_type	,		max_seq_length=data_args.max_seq_length	,		overwrite_cache=data_args.overwrite_cache	,		mode=Split.train	,		)
      if training_args.do_train
      else None
  )
  lowercase__			    =						(
      TokenClassificationDataset(
          token_classification_task=UpperCAmelCase__	,		data_dir=data_args.data_dir	,		tokenizer=UpperCAmelCase__	,		labels=UpperCAmelCase__	,		model_type=config.model_type	,		max_seq_length=data_args.max_seq_length	,		overwrite_cache=data_args.overwrite_cache	,		mode=Split.dev	,		)
      if training_args.do_eval
      else None
  )

  def align_predictions(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	) -> Tuple[List[int], List[int]]:
    lowercase__			    =						np.argmax(UpperCAmelCase__	,		axis=2	)

    lowercase__    ,					lowercase__			    =						preds.shape

    lowercase__			    =						[[] for _ in range(UpperCAmelCase__	)]
    lowercase__			    =						[[] for _ in range(UpperCAmelCase__	)]

    for i in range(UpperCAmelCase__	):
      for j in range(UpperCAmelCase__	):
        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
          out_label_list[i].append(label_map[label_ids[i][j]]	)
          preds_list[i].append(label_map[preds[i][j]]	)

    return preds_list, out_label_list

  def compute_metrics(_SCREAMING_SNAKE_CASE	) -> Dict:
    lowercase__    ,					lowercase__			    =						align_predictions(p.predictions	,		p.label_ids	)
    return {
        "accuracy_score": accuracy_score(UpperCAmelCase__	,		UpperCAmelCase__	),
        "precision": precision_score(UpperCAmelCase__	,		UpperCAmelCase__	),
        "recall": recall_score(UpperCAmelCase__	,		UpperCAmelCase__	),
        "f1": fa_score(UpperCAmelCase__	,		UpperCAmelCase__	),
    }

  # Data collator
  lowercase__			    =						DataCollatorWithPadding(UpperCAmelCase__	,		pad_to_multiple_of=8	) if training_args.fpaa else None

  # Initialize our Trainer
  lowercase__			    =						Trainer(
      model=UpperCAmelCase__	,		args=UpperCAmelCase__	,		train_dataset=UpperCAmelCase__	,		eval_dataset=UpperCAmelCase__	,		compute_metrics=UpperCAmelCase__	,		data_collator=UpperCAmelCase__	,		)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_process_zero():
      tokenizer.save_pretrained(training_args.output_dir	)

       # Evaluation
  lowercase__			    =						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'	)

    lowercase__			    =						trainer.evaluate()

    lowercase__			    =						os.path.join(training_args.output_dir	,		'eval_results.txt'	)
    if trainer.is_world_process_zero():
      with open(UpperCAmelCase__	,		'w'	) as writer:
        logger.info('***** Eval results *****'	)
        for key, value in result.items():
          logger.info('  %s = %s'	,		UpperCAmelCase__	,		UpperCAmelCase__	)
          writer.write('%s = %s\n' % (key, value)	)

      results.update(UpperCAmelCase__	)

       # Predict
  if training_args.do_predict:
    lowercase__			    =						TokenClassificationDataset(
        token_classification_task=UpperCAmelCase__	,		data_dir=data_args.data_dir	,		tokenizer=UpperCAmelCase__	,		labels=UpperCAmelCase__	,		model_type=config.model_type	,		max_seq_length=data_args.max_seq_length	,		overwrite_cache=data_args.overwrite_cache	,		mode=Split.test	,		)

    lowercase__    ,					lowercase__    ,					lowercase__			    =						trainer.predict(UpperCAmelCase__	)
    lowercase__    ,					lowercase__			    =						align_predictions(UpperCAmelCase__	,		UpperCAmelCase__	)

    lowercase__			    =						os.path.join(training_args.output_dir	,		'test_results.txt'	)
    if trainer.is_world_process_zero():
      with open(UpperCAmelCase__	,		'w'	) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'	,		UpperCAmelCase__	,		UpperCAmelCase__	)
          writer.write('%s = %s\n' % (key, value)	)

             # Save predictions
    lowercase__			    =						os.path.join(training_args.output_dir	,		'test_predictions.txt'	)
    if trainer.is_world_process_zero():
      with open(UpperCAmelCase__	,		'w'	) as writer:
        with open(os.path.join(data_args.data_dir	,		'test.txt'	)	,		'r'	) as f:
          token_classification_task.write_predictions_to_file(UpperCAmelCase__	,		UpperCAmelCase__	,		UpperCAmelCase__	)

  return results




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  main()


if __name__ == "__main__":
      main()









import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Optional[Any]    ,  a    :       UNetaDModel    ,  a    :       UNetaDModel    ,  a    :       DDPMScheduler    ,  a    :       Any    ,  )->     Dict:





     """simple docstring"""







     super().__init__()
     lowercase__			    =						value_function
     lowercase__			    =						unet
     lowercase__			    =						scheduler
     lowercase__			    =						env
     lowercase__			    =						env.get_dataset()
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].mean()
       except:  # noqa: E722
         pass
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].std()
       except:  # noqa: E722
         pass
     lowercase__			    =						env.observation_space.shape[0]
     lowercase__			    =						env.action_space.shape[0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Any    ,  a    :       int )->     Dict:





     """simple docstring"""







     return (x_in - self.means[key]) / self.stds[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







     return x_in * self.stds[key] + self.means[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Tuple )->     Tuple:





     """simple docstring"""







     if type(a ) is dict:
       return {k: self.to_torch(a ) for k, v in x_in.items()}
     elif torch.is_tensor(a ):
       return x_in.to(self.unet.device )
     return torch.tensor(a    ,  device=self.unet.device )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Optional[int]    ,  a    :       Dict    ,  a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     for key, val in cond.items():
       lowercase__			    =						val.clone()
     return x_in


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       Optional[Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						x.shape[0]
     lowercase__			    =						None
     for i in tqdm.tqdm(self.scheduler.timesteps ):
       # create batch of timesteps to pass into model
       lowercase__			    =						torch.full((batch_size,)    ,  a    ,  device=self.unet.device    ,  dtype=torch.long )
       for _ in range(a ):
         with torch.enable_grad():
           x.requires_grad_()

           # permute to match dimension for pre-trained models
           lowercase__			    =						self.value_function(x.permute(0    ,  2    ,  1 )    ,  a ).sample
           lowercase__			    =						torch.autograd.grad([y.sum()]    ,  [x] )[0]

           lowercase__			    =						self.scheduler._get_variance(a )
           lowercase__			    =						torch.exp(0.5 * posterior_variance )
           lowercase__			    =						model_std * grad

         lowercase__			    =						0
         lowercase__			    =						x.detach()
         lowercase__			    =						x + scale * grad
         lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )

       lowercase__			    =						self.unet(x.permute(0    ,  2    ,  1 )    ,  a ).sample.permute(0    ,  2    ,  1 )

       # TODO: verify deprecation of this kwarg
       lowercase__			    =						self.scheduler.step(a    ,  a    ,  a    ,  predict_epsilon=a )['prev_sample']

       # apply conditions to the trajectory (set the initial state)
       lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
       lowercase__			    =						self.to_torch(a )
     return x, y


   def __call__(					self    :       Any    ,  a    :       Tuple    ,  a    :       int=64    ,  a    :       Tuple=32    ,  a    :       List[Any]=2    ,  a    :       List[str]=0.1 )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.normalize(a    ,  'observations' )
     lowercase__			    =						obs[None].repeat(a    ,  axis=0 )

     lowercase__			    =						{0: self.to_torch(a )}
     lowercase__			    =						(batch_size, planning_horizon, self.state_dim + self.action_dim)

     # generate initial noise and apply our conditions (to make the trajectories start at current state)
     lowercase__			    =						randn_tensor(a    ,  device=self.unet.device )
     lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
     lowercase__			    =						self.to_torch(a )

     # run the diffusion process
     lowercase__    ,					lowercase__			    =						self.run_diffusion(a    ,  a    ,  a    ,  a )

     # sort output trajectories by value
     lowercase__			    =						y.argsort(0    ,  descending=a ).squeeze()
     lowercase__			    =						x[sorted_idx]
     lowercase__			    =						sorted_values[:, :, : self.action_dim]
     lowercase__			    =						actions.detach().cpu().numpy()
     lowercase__			    =						self.de_normalize(a    ,  key='actions' )

     # select the action with the highest value
     if y is not None:
       lowercase__			    =						0
     else:
       # if we didn't run value guiding, select a random action
       lowercase__			    =						np.random.randint(0    ,  a )

     lowercase__			    =						denorm_actions[selected_index, 0]
     return denorm_actions


