
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 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE      :							str	       =      field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default="NER"    ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
   _SCREAMING_SNAKE_CASE      :							bool	       =      field(default=__lowercase    ,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      :							Optional[str]	       =      field(
       default=__lowercase    ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,)





@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE      :							str	       =      field(
       metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}    ,)
   _SCREAMING_SNAKE_CASE      :							int	       =      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."
           )
       }    ,)
   _SCREAMING_SNAKE_CASE      :							bool	       =      field(
       default=__lowercase    ,metadata={"help": "Overwrite the cached training and evaluation sets"}    )





def    __UpperCamelCase     (				):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 __a :  Dict						=  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.
  __a				,       __a				,       __a :  Union[str, Any]						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
 else:
  __a				,       __a				,       __a :  List[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 :  int						=  import_module('''tasks'''  )
 try:
  __a :  int						=  getattr(lowerCAmelCase__  ,   model_args.task_type  )
  __a :  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 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'''  ,   lowerCAmelCase__  )

 # Set seed
 set_seed(training_args.seed  )

 # Prepare CONLL-2003 task
 __a :  Any						=  token_classification_task.get_labels(data_args.labels  )
 __a :  Dict[int, str]						=  dict(enumerate(lowerCAmelCase__  )  )
 __a :  Tuple						=  len(lowerCAmelCase__  )

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

 __a :  str						=  AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,   num_labels=lowerCAmelCase__  ,   idalabel=lowerCAmelCase__  ,   labelaid={label: i for i, label in enumerate(lowerCAmelCase__  )}  ,   cache_dir=model_args.cache_dir  ,   )
 __a :  Any						=  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  ,   )
 __a :  Union[str, Any]						=  AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path  ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )  ,   config=lowerCAmelCase__  ,   cache_dir=model_args.cache_dir  ,   )

 # Get datasets
 __a :  Optional[int]						=  (
     TokenClassificationDataset(
         token_classification_task=lowerCAmelCase__  ,   data_dir=data_args.data_dir  ,   tokenizer=lowerCAmelCase__  ,   labels=lowerCAmelCase__  ,   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 :  Any						=  (
     TokenClassificationDataset(
         token_classification_task=lowerCAmelCase__  ,   data_dir=data_args.data_dir  ,   tokenizer=lowerCAmelCase__  ,   labels=lowerCAmelCase__  ,   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(lowerCAmelCase__							:				np.ndarray  ,   lowerCAmelCase__							:				np.ndarray  ) -> Tuple[List[int], List[int]]:
  __a :  Tuple						=  np.argmax(lowerCAmelCase__  ,   axis=2  )

  __a				,       __a :  Dict						=  preds.shape

  __a :  List[Any]						=  [[] for _ in range(lowerCAmelCase__  )]
  __a :  Optional[int]						=  [[] for _ in range(lowerCAmelCase__  )]

  for i in range(lowerCAmelCase__  ):
   for j in range(lowerCAmelCase__  ):
    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(lowerCAmelCase__							:				EvalPrediction  ) -> Dict:
  __a				,       __a :  str						=  align_predictions(p.predictions  ,   p.label_ids  )
  return {
      "accuracy_score": accuracy_score(lowerCAmelCase__  ,   lowerCAmelCase__  ),
      "precision": precision_score(lowerCAmelCase__  ,   lowerCAmelCase__  ),
      "recall": recall_score(lowerCAmelCase__  ,   lowerCAmelCase__  ),
      "f1": fa_score(lowerCAmelCase__  ,   lowerCAmelCase__  ),
  }

 # Data collator
 __a :  List[Any]						=  DataCollatorWithPadding(lowerCAmelCase__  ,   pad_to_multiple_of=8  ) if training_args.fpaa else None

 # Initialize our Trainer
 __a :  Tuple						=  Trainer(
     model=lowerCAmelCase__  ,   args=lowerCAmelCase__  ,   train_dataset=lowerCAmelCase__  ,   eval_dataset=lowerCAmelCase__  ,   compute_metrics=lowerCAmelCase__  ,   data_collator=lowerCAmelCase__  ,   )

 # 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
 __a :  Optional[Any]						=  {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''  )

  __a :  Optional[int]						=  trainer.evaluate()

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

   results.update(lowerCAmelCase__  )

    # Predict
 if training_args.do_predict:
  __a :  Tuple						=  TokenClassificationDataset(
      token_classification_task=lowerCAmelCase__  ,   data_dir=data_args.data_dir  ,   tokenizer=lowerCAmelCase__  ,   labels=lowerCAmelCase__  ,   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 :  List[Any]						=  trainer.predict(lowerCAmelCase__  )
  __a				,       __a :  Dict						=  align_predictions(lowerCAmelCase__  ,   lowerCAmelCase__  )

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

        # Save predictions
  __a :  Union[str, Any]						=  os.path.join(training_args.output_dir  ,   '''test_predictions.txt'''  )
  if trainer.is_world_process_zero():
   with open(lowerCAmelCase__  ,   '''w'''  ) as writer:
    with open(os.path.join(data_args.data_dir  ,   '''test.txt'''  )  ,   '''r'''  ) as f:
     token_classification_task.write_predictions_to_file(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

 return results




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  img.load()

 # loop through the image-coordinates
 for image_x in range(lowerCAmelCase__  ):
  for image_y in range(lowerCAmelCase__  ):
   # determine the figure-coordinates based on the image-coordinates
   __a :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =get_image()

      # uncomment for colored version, different section, zoomed in
      # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
      # figure_width = 0.8)

      # uncomment for black and white version, full figure
      # img = get_image(use_distance_color_coding = False)

      # uncomment to save the image
      # img.save("mandelbrot.png")

      img.show()
