




import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__UpperCamelCase						:       List[Any]							=    logging.getLogger(__name__)

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							str							=					field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
   __SCREAMING_SNAKE_CASE		:							Any							=					field(
       default=__UpperCAmelCase   ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
   __SCREAMING_SNAKE_CASE		:							str							=					field(
       default="NER"   ,					metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}   )
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					field(
       default=__UpperCAmelCase   ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
   __SCREAMING_SNAKE_CASE		:							Tuple							=					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.
   __SCREAMING_SNAKE_CASE		:							str							=					field(
       default=__UpperCAmelCase   ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,					)

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					field(
       metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}   )
   __SCREAMING_SNAKE_CASE		:							Tuple							=					field(
       metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}   )
   __SCREAMING_SNAKE_CASE		:							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."
           )
       }   ,					)
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					field(
       default=__UpperCAmelCase   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )


def 		_lowerCamelCase				(					)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
  _lowercase: List[Any]						     =    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: Optional[Any]						     =    import_module('tasks'							)

  try:
    _lowercase: List[Any]						     =    getattr(lowercase_					,     model_args.task_type							)
    _lowercase: TokenClassificationTask						     =    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					,     )
  logger.info(
      'n_replicas: %s, distributed training: %s, 16-bits training: %s'					,     training_args.n_replicas					,     bool(training_args.n_replicas > 1							)					,     training_args.fpaa					,     )
  logger.info('Training/evaluation parameters %s'					,     lowercase_							)

  # Prepare Token Classification task
  _lowercase: Optional[int]						     =    token_classification_task.get_labels(data_args.labels							)
  _lowercase: Dict[int, str]						     =    dict(enumerate(lowercase_							)							)
  _lowercase: Any						     =    len(lowercase_							)

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

  _lowercase: Dict						     =    AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path					,     num_labels=lowercase_					,     idalabel=lowercase_					,     labelaid={label: i for i, label in enumerate(lowercase_							)}					,     cache_dir=model_args.cache_dir					,     )
  _lowercase: Optional[int]						     =    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					,     )

  with training_args.strategy.scope():
    _lowercase: List[str]						     =    TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path					,     from_pt=bool('.bin' in model_args.model_name_or_path							)					,     config=lowercase_					,     cache_dir=model_args.cache_dir					,     )

  # Get datasets
  _lowercase: Dict						     =    (
      TFTokenClassificationDataset(
          token_classification_task=lowercase_					,     data_dir=data_args.data_dir					,     tokenizer=lowercase_					,     labels=lowercase_					,     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: List[Any]						     =    (
      TFTokenClassificationDataset(
          token_classification_task=lowercase_					,     data_dir=data_args.data_dir					,     tokenizer=lowercase_					,     labels=lowercase_					,     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(UpperCamelCase					,     UpperCamelCase							) -> Tuple[List[int], List[int]]:
    _lowercase: str						     =    np.argmax(lowercase_					,     axis=2							)
    _lowercase: Optional[int]						     =    preds.shape
    _lowercase: Any						     =    [[] for _ in range(lowercase_							)]
    _lowercase: List[str]						     =    [[] for _ in range(lowercase_							)]

    for i in range(lowercase_							):
      for j in range(lowercase_							):
        if label_ids[i, j] != -100:
          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(UpperCamelCase							) -> Dict:
    _lowercase: Tuple						     =    align_predictions(p.predictions					,     p.label_ids							)

    return {
        "precision": precision_score(lowercase_					,     lowercase_							),
        "recall": recall_score(lowercase_					,     lowercase_							),
        "f1": fa_score(lowercase_					,     lowercase_							),
    }

  # Initialize our Trainer
  _lowercase: Dict						     =    TFTrainer(
      model=lowercase_					,     args=lowercase_					,     train_dataset=train_dataset.get_dataset() if train_dataset else None					,     eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,     compute_metrics=lowercase_					,     )

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir							)

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

    _lowercase: List[str]						     =    trainer.evaluate()
    _lowercase: Optional[Any]						     =    os.path.join(training_args.output_dir					,     'eval_results.txt'							)

    with open(lowercase_					,     'w'							) as writer:
      logger.info('***** Eval results *****'							)

      for key, value in result.items():
        logger.info('  %s = %s'					,     lowercase_					,     lowercase_							)
        writer.write('%s = %s\n' % (key, value)							)

      results.update(lowercase_							)

    # Predict
  if training_args.do_predict:
    _lowercase: Union[str, Any]						     =    TFTokenClassificationDataset(
        token_classification_task=lowercase_					,     data_dir=data_args.data_dir					,     tokenizer=lowercase_					,     labels=lowercase_					,     model_type=config.model_type					,     max_seq_length=data_args.max_seq_length					,     overwrite_cache=data_args.overwrite_cache					,     mode=Split.test					,     )

    _lowercase: Dict						     =    trainer.predict(test_dataset.get_dataset()							)
    _lowercase: List[Any]						     =    align_predictions(lowercase_					,     lowercase_							)
    _lowercase: List[Any]						     =    classification_report(lowercase_					,     lowercase_							)

    logger.info('\n%s'					,     lowercase_							)

    _lowercase: Any						     =    os.path.join(training_args.output_dir					,     'test_results.txt'							)

    with open(lowercase_					,     'w'							) as writer:
      writer.write('%s\n' % report							)

    # Save predictions
    _lowercase: Union[str, Any]						     =    os.path.join(training_args.output_dir					,     'test_predictions.txt'							)

    with open(lowercase_					,     'w'							) as writer:
      with open(os.path.join(data_args.data_dir					,     'test.txt'							)					,     'r'							) as f:
        _lowercase: List[str]						     =    0

        for line in f:
          if line.startswith('-DOCSTART-'							) or line == "" or line == "\n":
            writer.write(lowercase_							)

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            _lowercase: Any						     =    line.split()[0] + ''' ''' + preds_list[example_id].pop(0							) + '''\n'''

            writer.write(lowercase_							)
          else:
            logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'					,     line.split()[0]							)

  return results


if __name__ == "__main__":
   main()






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    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
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
