





"""simple docstring"""




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()


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

@dataclass
class 					_UpperCAmelCase    :



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


@dataclass
class 					_UpperCAmelCase    :



      '''simple docstring'''
      lowercase_ :					List[Any]             =  field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}						)
      lowercase_ :					int             =  field(
          metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						)
      lowercase_ :					Tuple             =  field(
          default=128     ,  metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }     ,  )
      lowercase_ :					Union[str, Any]             =  field(
          default=__snake_case     ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
  A_		,			A_		,			A_    :			str									=    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.'   )

  A_    :			Dict									=    import_module('tasks'   )

  try:
    A_    :			Dict									=    getattr(snake_case__					,   model_args.task_type   )
    A_    :			Union[str, Any]									=    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'					,   snake_case__   )

  # Prepare Token Classification task
  A_    :			str									=    token_classification_task.get_labels(data_args.labels   )
  A_    :			Dict									=    dict(enumerate(snake_case__   )   )
  A_    :			Tuple									=    len(snake_case__   )

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

  A_    :			Optional[int]									=    AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path					,   num_labels=snake_case__					,   idalabel=snake_case__					,   labelaid={label: i for i, label in enumerate(snake_case__   )}					,   cache_dir=model_args.cache_dir					,   )
  A_    :			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():
    A_    :			List[Any]									=    TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path					,   from_pt=bool('.bin' in model_args.model_name_or_path   )					,   config=snake_case__					,   cache_dir=model_args.cache_dir					,   )

  # Get datasets
  A_    :			List[Any]									=    (
      TFTokenClassificationDataset(
          token_classification_task=snake_case__					,   data_dir=data_args.data_dir					,   tokenizer=snake_case__					,   labels=snake_case__					,   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
  )
  A_    :			Optional[int]									=    (
      TFTokenClassificationDataset(
          token_classification_task=snake_case__					,   data_dir=data_args.data_dir					,   tokenizer=snake_case__					,   labels=snake_case__					,   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]]:
    A_    :			str									=    np.argmax(snake_case__					,   axis=2   )
    A_		,			A_    :			List[Any]									=    preds.shape
    A_    :			Tuple									=    [[] for _ in range(snake_case__   )]
    A_    :			Union[str, Any]									=    [[] for _ in range(snake_case__   )]

    for i in range(snake_case__   ):
      for j in range(snake_case__   ):
        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:
    A_		,			A_    :			int									=    align_predictions(p.predictions					,   p.label_ids   )

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

  # Initialize our Trainer
  A_    :			Optional[Any]									=    TFTrainer(
      model=snake_case__					,   args=snake_case__					,   train_dataset=train_dataset.get_dataset() if train_dataset else None					,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,   compute_metrics=snake_case__					,   )

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

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

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

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

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

      results.update(snake_case__   )

    # Predict
  if training_args.do_predict:
    A_    :			int									=    TFTokenClassificationDataset(
        token_classification_task=snake_case__					,   data_dir=data_args.data_dir					,   tokenizer=snake_case__					,   labels=snake_case__					,   model_type=config.model_type					,   max_seq_length=data_args.max_seq_length					,   overwrite_cache=data_args.overwrite_cache					,   mode=Split.test					,   )

    A_		,			A_		,			A_    :			Tuple									=    trainer.predict(test_dataset.get_dataset()   )
    A_		,			A_    :			Optional[Any]									=    align_predictions(snake_case__					,   snake_case__   )
    A_    :			List[str]									=    classification_report(snake_case__					,   snake_case__   )

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

    A_    :			Optional[Any]									=    os.path.join(training_args.output_dir					,   'test_results.txt'   )

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

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

    with open(snake_case__					,   'w'   ) as writer:
      with open(os.path.join(data_args.data_dir					,   'test.txt'   )					,   'r'   ) as f:
        A_    :			Union[str, Any]									=    0

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

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

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

  return results


if __name__ == "__main__":
   main()

import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__magic_name__  :  Dict     			=   1_0_0_0


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=1.2						,    __UpperCamelCase="g"						,    __UpperCamelCase=0.5						,    __UpperCamelCase="-"						,    __UpperCamelCase="test_out.jpg"						,    __UpperCamelCase=True						,    __UpperCamelCase=False						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=0.7						,    ):
      if isinstance(__UpperCamelCase						,    torch.Tensor					):
          A_								=					img.numpy().astype("np.uint8"					)
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					img_tensorize(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    np.ndarray					)

      A_	,	A_								=					img.shape[1], img.shape[0]
      A_								=					mplfigure.Figure(frameon=__UpperCamelCase					)
      A_								=					fig.get_dpi()
      A_								=					(width * scale + 1E-2) / dpi
      A_								=					(height * scale + 1E-2) / dpi
      fig.set_size_inches(__UpperCamelCase						,    __UpperCamelCase					)
      A_								=					fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
      ax.axis("off"					)
      ax.set_xlim(0.0						,    __UpperCamelCase					)
      ax.set_ylim(__UpperCamelCase					)

      A_								=					saveas
      A_								=					rgb
      A_								=					pynb
      A_								=					img
      A_								=					edgecolor
      A_								=					0.5
      A_								=					linestyle
      A_								=					int(np.sqrt(min(__UpperCamelCase						,    __UpperCamelCase					)					) * scale // 3					)
      A_								=					width
      A_								=					height
      A_								=					scale
      A_								=					fig
      A_								=					ax
      A_								=					pad
      A_								=					idaobj
      A_								=					idaattr
      A_								=					FigureCanvasAgg(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      if color is None:
          A_								=					self.edgecolor
      ((A_)	,	(A_)	,	(A_)	,	(A_))								=					box
      A_								=					xa - xa
      A_								=					ya - ya
      self.ax.add_patch(
          mpl.patches.Rectangle(
              (xa, ya)						,    __UpperCamelCase						,    __UpperCamelCase						,    fill=__UpperCamelCase						,    edgecolor=__UpperCamelCase						,    linewidth=self.font_size // 3						,    alpha=self.alpha						,    linestyle=self.linestyle						,    )					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None					):
      if len(boxes.shape					) > 2:
          A_								=					boxes[0]
      if len(obj_ids.shape					) > 1:
          A_								=					obj_ids[0]
      if len(obj_scores.shape					) > 1:
          A_								=					obj_scores[0]
      if len(attr_ids.shape					) > 1:
          A_								=					attr_ids[0]
      if len(attr_scores.shape					) > 1:
          A_								=					attr_scores[0]
      if isinstance(__UpperCamelCase						,    torch.Tensor					):
          A_								=					boxes.numpy()
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					np.array(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    np.ndarray					)
      A_								=					np.prod(boxes[:, 2:] - boxes[:, :2]						,    axis=1					)
      A_								=					np.argsort(-areas					).tolist()
      A_								=					boxes[sorted_idxs] if boxes is not None else None
      A_								=					obj_ids[sorted_idxs] if obj_ids is not None else None
      A_								=					obj_scores[sorted_idxs] if obj_scores is not None else None
      A_								=					attr_ids[sorted_idxs] if attr_ids is not None else None
      A_								=					attr_scores[sorted_idxs] if attr_scores is not None else None

      A_								=					[self._random_color(maximum=1					) for _ in range(len(__UpperCamelCase					)					)]
      A_								=					[assigned_colors[idx] for idx in sorted_idxs]
      if obj_ids is not None:
          A_								=					self._create_text_labels_attr(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
          for i in range(len(__UpperCamelCase					)					):
              A_								=					assigned_colors[i]
              self.add_box(boxes[i]						,    __UpperCamelCase					)
              self.draw_labels(labels[i]						,    boxes[i]						,    __UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_	,	A_	,	A_	,	A_								=					box
      A_								=					(xa, ya)
      A_								=					(ya - ya) * (xa - xa)
      A_								=					_SMALL_OBJ * self.scale
      if instance_area < small or ya - ya < 40 * self.scale:
          if ya >= self.height - 5:
              A_								=					(xa, ya)
          else:
              A_								=					(xa, ya)

      A_								=					(ya - ya) / np.sqrt(self.height * self.width					)
      A_								=					self._change_color_brightness(__UpperCamelCase						,    brightness_factor=0.7					)
      A_								=					np.clip((height_ratio - 0.02) / 0.08 + 1						,    1.2						,    2					)
      font_size *= 0.75 * self.font_size

      self.draw_text(
          text=__UpperCamelCase						,    position=__UpperCamelCase						,    color=__UpperCamelCase						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase="g"						,    __UpperCamelCase="left"						,    ):
      A_								=					0
      A_								=					self.font_size
      A_								=					np.maximum(list(mplc.to_rgb(__UpperCamelCase					)					)						,    0.2					)
      A_								=					max(0.8						,    np.max(__UpperCamelCase					)					)
      A_								=					{
          "facecolor": "black",
          "alpha": self.alpha,
          "pad": self.pad,
          "edgecolor": "none",
      }
      A_	,	A_								=					position
      self.ax.text(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    size=font_size * self.scale						,    family="sans-serif"						,    bbox=__UpperCamelCase						,    verticalalignment="top"						,    horizontalalignment=__UpperCamelCase						,    color=__UpperCamelCase						,    zorder=10						,    rotation=__UpperCamelCase						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase=None					):
      if saveas is None:
          A_								=					self.saveas
      if saveas.lower().endswith(".jpg"					) or saveas.lower().endswith(".png"					):
          cva.imwrite(
              __UpperCamelCase						,    self._get_buffer()[:, :, ::-1]						,    )
      else:
          self.fig.savefig(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					[self.idaobj[i] for i in classes]
      A_								=					[self.idaattr[i] for i in attr_classes]
      A_								=					[
          f'{label} {score:.2f} {attr} {attr_score:.2f}'
          for label, score, attr, attr_score in zip(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      ]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					[self.idaobj[i] for i in classes]
      if scores is not None:
          if labels is None:
              A_								=					["{:.0f}%".format(s * 100					) for s in scores]
          else:
              A_								=					["{} {:.0f}%".format(__UpperCamelCase						,    s * 100					) for li, s in zip(__UpperCamelCase						,    __UpperCamelCase					)]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase=255					):
      A_								=					np.random.randint(0						,    len(_COLORS					)					)
      A_								=					_COLORS[idx] * maximum
      if not self.rgb:
          A_								=					ret[::-1]
      return ret

  def 					lowercase_						(					self					):
      if not self.pynb:
          A_	,	A_								=					self.canvas.print_to_buffer()
          if (width, height) != (self.width, self.height):
              A_								=					cva.resize(self.img						,    (width, height)					)
          else:
              A_								=					self.img
      else:
          A_								=					io.BytesIO()  # works for cairo backend
          self.canvas.print_rgba(__UpperCamelCase					)
          A_	,	A_								=					self.width, self.height
          A_								=					buf.getvalue()
          A_								=					self.img

      A_								=					np.frombuffer(__UpperCamelCase						,    dtype="uint8"					)
      A_								=					buffer.reshape(__UpperCamelCase						,    __UpperCamelCase						,    4					)
      A_	,	A_								=					np.split(__UpperCamelCase						,    [3]						,    axis=2					)

      try:
          import numexpr as ne  # fuse them with numexpr

          A_								=					ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"					)
      except ImportError:
          A_								=					alpha.astype("float32"					) / 255.0
          A_								=					img * (1 - alpha) + rgb * alpha

      return visualized_image.astype("uint8"					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      assert brightness_factor >= -1.0 and brightness_factor <= 1.0
      A_								=					mplc.to_rgb(__UpperCamelCase					)
      A_								=					colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCamelCase					)					)
      A_								=					polygon_color[1] + (brightness_factor * polygon_color[1])
      A_								=					0.0 if modified_lightness < 0.0 else modified_lightness
      A_								=					1.0 if modified_lightness > 1.0 else modified_lightness
      A_								=					colorsys.hls_to_rgb(polygon_color[0]						,    __UpperCamelCase						,    polygon_color[2]					)
      return modified_color



__magic_name__  :  List[str]     			=   (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
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    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






