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


a  :      int            =    logging.getLogger(__name__)

@dataclass
class 					_lowercase    :






    '''simple docstring'''
    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=UpperCAmelCase__ ,  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=UpperCAmelCase__ ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
    SCREAMING_SNAKE_CASE:				bool          =    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:				Optional[str]          =    field(
        default=UpperCAmelCase__ ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,  )





@dataclass
class 					_lowercase    :






    '''simple docstring'''
    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=UpperCAmelCase__ ,  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=UpperCAmelCase__ ,  metadata={'help': 'Overwrite the cached training and evaluation sets'}			)





def 		snake_case__	(		):
   # 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.

   lowerCAmelCase_:							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.
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							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."					)

   lowerCAmelCase_:							Optional[Any]       =	import_module("tasks"					)
   try:
      lowerCAmelCase_:							Optional[Any]       =	getattr(lowercase							,				model_args.task_type					)
      lowerCAmelCase_:							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"							,				lowercase					)

   # Set seed
   set_seed(training_args.seed					)

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

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

   lowerCAmelCase_:							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							,				)
   lowerCAmelCase_:							Union[str, 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							,				)
   lowerCAmelCase_:							Any       =	AutoModelForTokenClassification.from_pretrained(
       model_args.model_name_or_path							,				from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,				config=lowercase							,				cache_dir=model_args.cache_dir							,				)

   # Get datasets
   lowerCAmelCase_:							Any       =	(
       TokenClassificationDataset(
           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
   )
   lowerCAmelCase_:							Optional[Any]       =	(
       TokenClassificationDataset(
           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(lowercase							,				lowercase					) -> Tuple[List[int], List[int]]:
      lowerCAmelCase_:							str       =	np.argmax(lowercase							,				axis=2					)

      lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	preds.shape

      lowerCAmelCase_:							Optional[Any]       =	[[] for _ in range(lowercase					)]
      lowerCAmelCase_:							Tuple       =	[[] for _ in range(lowercase					)]

      for i in range(lowercase					):
         for j in range(lowercase					):
            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(lowercase					) -> Dict:
      lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	align_predictions(p.predictions							,				p.label_ids					)
      return {
          "accuracy_score": accuracy_score(lowercase							,				lowercase					),
          "precision": precision_score(lowercase							,				lowercase					),
          "recall": recall_score(lowercase							,				lowercase					),
          "f1": fa_score(lowercase							,				lowercase					),
      }

   # Data collator
   lowerCAmelCase_:							List[str]       =	DataCollatorWithPadding(lowercase							,				pad_to_multiple_of=8					) if training_args.fpaa else None

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

   # 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
   lowerCAmelCase_:							int       =	{}
   if training_args.do_eval:
      logger.info("*** Evaluate ***"					)

      lowerCAmelCase_:							Union[str, Any]       =	trainer.evaluate()

      lowerCAmelCase_:							List[str]       =	os.path.join(training_args.output_dir							,				"eval_results.txt"					)
      if trainer.is_world_process_zero():
         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:
      lowerCAmelCase_:							Tuple       =	TokenClassificationDataset(
          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							,				)

      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	trainer.predict(lowercase					)
      lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	align_predictions(lowercase							,				lowercase					)

      lowerCAmelCase_:							Optional[int]       =	os.path.join(training_args.output_dir							,				"test_results.txt"					)
      if trainer.is_world_process_zero():
         with open(lowercase							,				"w"					) as writer:
            for key, value in metrics.items():
               logger.info("  %s = %s"							,				lowercase							,				lowercase					)
               writer.write("%s = %s\n" % (key, value)					)

        # Save predictions
      lowerCAmelCase_:							Optional[Any]       =	os.path.join(training_args.output_dir							,				"test_predictions.txt"					)
      if trainer.is_world_process_zero():
         with open(lowercase							,				"w"					) as writer:
            with open(os.path.join(data_args.data_dir							,				"test.txt"					)							,				"r"					) as f:
               token_classification_task.write_predictions_to_file(lowercase							,				lowercase							,				lowercase					)

   return results
def 		snake_case__	(		lowercase					):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]
   lowerCAmelCase_:							Optional[Any]       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1 or len(lowercase					) <= key:
      return input_string

   for position, character in enumerate(lowercase					):
      lowerCAmelCase_:							Optional[Any]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Any       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	["".join(lowercase					) for row in temp_grid]
   lowerCAmelCase_:							int       =	"".join(lowercase					)

   return output_string
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	[]
   lowerCAmelCase_:							str       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1:
      return input_string

   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]  # generates template
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							List[str]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append("*"					)

   lowerCAmelCase_:							Optional[Any]       =	0
   for row in temp_grid:  # fills in the characters
      lowerCAmelCase_:							Tuple       =	input_string[counter : counter + len(lowercase					)]
      grid.append(list(lowercase					)					)
      counter += len(lowercase					)

   lowerCAmelCase_:							int       =	""  # reads as zigzag
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							str       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      output_string += grid[num][0]
      grid[num].pop(0					)
   return output_string
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Dict       =	{}
   for key_guess in range(1							,				len(lowercase					)					):  # tries every key
      lowerCAmelCase_:							int       =	decrypt(lowercase							,				lowercase					)
   return results


if __name__ == "__main__":
 import doctest

 doctest.testmod()