



"""simple docstring"""
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:     Optional[Any]										=	logging.getLogger(__name__)



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



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     str		=		field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}				)
   __lowerCAmelCase						:     Optional[str]		=		field(
       default=UpperCAmelCase__   ,    metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}   ,    )
   __lowerCAmelCase						:     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.'
           )
       }   ,    )
   __lowerCAmelCase						:     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.

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

 UpperCAmelCase			:      Tuple			=			import_module("""tasks"""		)
 try:
  UpperCAmelCase			:      int			=			getattr(UpperCamelCase    ,	model_args.task_type		)
  UpperCAmelCase			:      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"""    ,	UpperCamelCase		)

 # Set seed
 set_seed(training_args.seed		)

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

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

 UpperCAmelCase			:      List[str]			=			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    ,	)
 UpperCAmelCase			:      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    ,	)
 UpperCAmelCase			:      Optional[Any]			=			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
 UpperCAmelCase			:      List[str]			=			(
     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
 )
 UpperCAmelCase			:      List[Any]			=			(
     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(UpperCamelCase		:	np.ndarray    ,	UpperCamelCase		:	np.ndarray		) -> Tuple[List[int], List[int]]:
  UpperCAmelCase			:      Tuple			=			np.argmax(UpperCamelCase    ,	axis=2		)

  UpperCAmelCase			,	UpperCAmelCase			:      str			=			preds.shape

  UpperCAmelCase			:      int			=			[[] for _ in range(UpperCamelCase		)]
  UpperCAmelCase			:      Optional[Any]			=			[[] 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(UpperCamelCase		:	EvalPrediction		) -> Dict:
  UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			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
 UpperCAmelCase			:      Optional[int]			=			DataCollatorWithPadding(UpperCamelCase    ,	pad_to_multiple_of=8		) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCAmelCase			:      List[Any]			=			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
 UpperCAmelCase			:      List[Any]			=			{}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""		)

  UpperCAmelCase			:      str			=			trainer.evaluate()

  UpperCAmelCase			:      List[str]			=			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:
  UpperCAmelCase			:      int			=			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    ,	)

  UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			trainer.predict(UpperCamelCase		)
  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			align_predictions(UpperCamelCase    ,	UpperCamelCase		)

  UpperCAmelCase			:      int			=			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
  UpperCAmelCase			:      Tuple			=			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    _snake_case		(		UpperCamelCase		:	Optional[int]		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()





"""simple docstring"""
from __future__ import annotations

from decimal import Decimal

from numpy import array

def    _snake_case		(		UpperCamelCase		:	list[list[float]]		):
 UpperCAmelCase			:      str			=			Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(UpperCamelCase		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
  # Calculate the determinant of the matrix
  UpperCAmelCase			:      Optional[int]			=			float(
      d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
  if determinant == 0:
   raise ValueError("""This matrix has no inverse."""		)

  # Creates a copy of the matrix with swapped positions of the elements
  UpperCAmelCase			:      Optional[Any]			=			[[0.0, 0.0], [0.0, 0.0]]
  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			matrix[1][1], matrix[0][0]
  UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			-matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(UpperCamelCase		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(UpperCamelCase		) == 3
     and len(matrix[0]		) == 3
     and len(matrix[1]		) == 3
     and len(matrix[2]		) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  UpperCAmelCase			:      str			=			float(
      (
          (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
          + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
          + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
      )
      - (
          (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
          + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
          + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
      )		)
  if determinant == 0:
   raise ValueError("""This matrix has no inverse."""		)

  # Creating cofactor matrix
  UpperCAmelCase			:      Optional[int]			=			[
      [d(0.0		), d(0.0		), d(0.0		)],
      [d(0.0		), d(0.0		), d(0.0		)],
      [d(0.0		), d(0.0		), d(0.0		)],
  ]
  UpperCAmelCase			:      str			=			(d(matrix[1][1]		) * d(matrix[2][2]		)) - (
      d(matrix[1][2]		) * d(matrix[2][1]		)
  )
  UpperCAmelCase			:      List[str]			=			-(
      (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
  )
  UpperCAmelCase			:      Optional[int]			=			(d(matrix[1][0]		) * d(matrix[2][1]		)) - (
      d(matrix[1][1]		) * d(matrix[2][0]		)
  )
  UpperCAmelCase			:      Optional[int]			=			-(
      (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
  )
  UpperCAmelCase			:      str			=			(d(matrix[0][0]		) * d(matrix[2][2]		)) - (
      d(matrix[0][2]		) * d(matrix[2][0]		)
  )
  UpperCAmelCase			:      Union[str, Any]			=			-(
      (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
  )
  UpperCAmelCase			:      Dict			=			(d(matrix[0][1]		) * d(matrix[1][2]		)) - (
      d(matrix[0][2]		) * d(matrix[1][1]		)
  )
  UpperCAmelCase			:      Optional[Any]			=			-(
      (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
  )
  UpperCAmelCase			:      str			=			(d(matrix[0][0]		) * d(matrix[1][1]		)) - (
      d(matrix[0][1]		) * d(matrix[1][0]		)
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  UpperCAmelCase			:      str			=			array(UpperCamelCase		)
  for i in range(3		):
   for j in range(3		):
    UpperCAmelCase			:      Any			=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  UpperCAmelCase			:      Tuple			=			array(UpperCamelCase		)
  for i in range(3		):
   for j in range(3		):
    inverse_matrix[i][j] /= d(UpperCamelCase		)

        # Calculate the inverse of the matrix
  return [[float(d(UpperCamelCase		)		) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""		)
