




'''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


UpperCamelCase_       =							logging.getLogger(__name__)






@dataclass
class 				_a							:





       '''simple docstring'''






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






@dataclass
class 				_a							:





       '''simple docstring'''






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




def    lowercase__(       ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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.
            SCREAMING_SNAKE_CASE					:	Dict   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
      else:
            SCREAMING_SNAKE_CASE					:	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.'  )

      SCREAMING_SNAKE_CASE					:	List[str]   =		import_module('tasks'  )
      try:
            SCREAMING_SNAKE_CASE					:	Any   =		getattr(__UpperCamelCase	,model_args.task_type  )
            SCREAMING_SNAKE_CASE					:	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
      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		token_classification_task.get_labels(data_args.labels  )
      SCREAMING_SNAKE_CASE					:	Dict[int, str]   =		dict(enumerate(__UpperCamelCase  )  )
      SCREAMING_SNAKE_CASE					:	List[str]   =		len(__UpperCamelCase  )

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

      SCREAMING_SNAKE_CASE					:	int   =		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	,)
      SCREAMING_SNAKE_CASE					:	str   =		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	,)
      SCREAMING_SNAKE_CASE					:	int   =		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
      SCREAMING_SNAKE_CASE					:	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
      )
      SCREAMING_SNAKE_CASE					:	Optional[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: Tuple	,__UpperCamelCase: int  ) -> Tuple[List[int], List[int]]:
            SCREAMING_SNAKE_CASE					:	Dict   =		np.argmax(__UpperCamelCase	,axis=2  )

            SCREAMING_SNAKE_CASE					:	Any   =		preds.shape

            SCREAMING_SNAKE_CASE					:	List[str]   =		[[] for _ in range(__UpperCamelCase  )]
            SCREAMING_SNAKE_CASE					:	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: Optional[Any]  ) -> Dict:
            SCREAMING_SNAKE_CASE					:	str   =		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
      SCREAMING_SNAKE_CASE					:	str   =		DataCollatorWithPadding(__UpperCamelCase	,pad_to_multiple_of=8  ) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE					:	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
      SCREAMING_SNAKE_CASE					:	Any   =		{}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'  )

            SCREAMING_SNAKE_CASE					:	Dict   =		trainer.evaluate()

            SCREAMING_SNAKE_CASE					:	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:
            SCREAMING_SNAKE_CASE					:	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.test	,)

            SCREAMING_SNAKE_CASE					:	Optional[Any]   =		trainer.predict(__UpperCamelCase  )
            SCREAMING_SNAKE_CASE					:	Dict   =		align_predictions(__UpperCamelCase	,__UpperCamelCase  )

            SCREAMING_SNAKE_CASE					:	Tuple   =		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
            SCREAMING_SNAKE_CASE					:	Any   =		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    lowercase__(       __UpperCamelCase: Dict  ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()



import numpy as np



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 1e-12  ,			SCREAMING_SNAKE_CASE = 100  ,			):
    '''simple docstring'''







    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[1]
    # Ensure proper dimensionality.
    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[0]
    # Ensure inputs are either both complex or both real
    assert np.iscomplexobj(SCREAMING_SNAKE_CASE	) == np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    if is_complex:
        # Ensure complex input_matrix is Hermitian
        assert np.array_equal(SCREAMING_SNAKE_CASE  ,			input_matrix.conj().T	)

    # Set convergence to False. Will define convergence when we exceed max_iterations
    # or when we have small changes from one iteration to next.

    __UpperCamelCase     :str			  =		False
    __UpperCamelCase     :int			  =		0
    __UpperCamelCase     :Optional[Any]			  =		0
    __UpperCamelCase     :Union[str, Any]			  =		1e12

    while not convergence:
        # Multiple matrix by the vector.
        __UpperCamelCase     :List[str]			  =		np.dot(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        # Normalize the resulting output vector.
        __UpperCamelCase     :Tuple			  =		w / np.linalg.norm(SCREAMING_SNAKE_CASE	)
        # Find rayleigh quotient
        # (faster than usual b/c we know vector is normalized already)
        __UpperCamelCase     :int			  =		vector.conj().T if is_complex else vector.T
        __UpperCamelCase     :Optional[int]			  =		np.dot(SCREAMING_SNAKE_CASE  ,			np.dot(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)

        # Check convergence.
        __UpperCamelCase     :Optional[Any]			  =		np.abs(lambda_ - lambda_previous	) / lambda_
        iterations += 1

        if error <= error_tol or iterations >= max_iterations:
            __UpperCamelCase     :Dict			  =		True

        __UpperCamelCase     :List[Any]			  =		lambda_

    if is_complex:
        __UpperCamelCase     :Tuple			  =		np.real(lambda_	)

    return lambda_, vector



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]	)
    __UpperCamelCase     :Optional[Any]			  =		np.array([41, 4, 20]	)
    __UpperCamelCase     :Any			  =		real_input_matrix.astype(np.complexaaa	)
    __UpperCamelCase     :Dict			  =		np.triu(1j * complex_input_matrix  ,			1	)
    complex_input_matrix += imag_matrix
    complex_input_matrix += -1 * imag_matrix.T
    __UpperCamelCase     :Optional[int]			  =		np.array([41, 4, 20]	).astype(np.complexaaa	)

    for problem_type in ["real", "complex"]:
        if problem_type == "real":
            __UpperCamelCase     :Any			  =		real_input_matrix
            __UpperCamelCase     :int			  =		real_vector
        elif problem_type == "complex":
            __UpperCamelCase     :Tuple			  =		complex_input_matrix
            __UpperCamelCase     :Optional[Any]			  =		complex_vector

        # Our implementation.
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		power_iteration(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        # Numpy implementation.

        # Get eigenvalues and eigenvectors using built-in numpy
        # eigh (eigh used for symmetric or hermetian matrices).
        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		np.linalg.eigh(SCREAMING_SNAKE_CASE	)
        # Last eigenvalue is the maximum one.
        __UpperCamelCase     :List[Any]			  =		eigen_values[-1]
        # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
        __UpperCamelCase     :str			  =		eigen_vectors[:, -1]

        # Check our implementation and numpy gives close answers.
        assert np.abs(eigen_value - eigen_value_max	) <= 1e-6
        # Take absolute values element wise of each eigenvector.
        # as they are only unique to a minus sign.
        assert np.linalg.norm(np.abs(SCREAMING_SNAKE_CASE	) - np.abs(SCREAMING_SNAKE_CASE	)	) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_power_iteration()





