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





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}			)
   lowerCamelCase_            =field(
       metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}			)
   lowerCamelCase_            =field(
       default=1_2_8			,					metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Overwrite the cached training and evaluation sets'}			)




def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
      lowercase_			,				lowercase_			,				lowercase_     			=   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."					)

      lowercase_     			=   import_module("tasks"					)

      try:
            lowercase_     			=   getattr(__lowerCamelCase			,		model_args.task_type					)
            lowercase_     			=   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"			,		__lowerCamelCase					)

      # Prepare Token Classification task
      lowercase_     			=   token_classification_task.get_labels(data_args.labels					)
      lowercase_     			=   dict(enumerate(__lowerCamelCase					)					)
      lowercase_     			=   len(__lowerCamelCase					)

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

      lowercase_     			=   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			,		)
      lowercase_     			=   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():
            lowercase_     			=   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path			,		from_pt=bool(".bin" in model_args.model_name_or_path					)			,		config=__lowerCamelCase			,		cache_dir=model_args.cache_dir			,		)

      # Get datasets
      lowercase_     			=   (
          TFTokenClassificationDataset(
              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
      )
      lowercase_     			=   (
          TFTokenClassificationDataset(
              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]]:
            lowercase_     			=   np.argmax(__lowerCamelCase			,		axis=2					)
            lowercase_			,				lowercase_     			=   preds.shape
            lowercase_     			=   [[] for _ in range(__lowerCamelCase					)]
            lowercase_     			=   [[] for _ in range(__lowerCamelCase					)]

            for i in range(__lowerCamelCase					):
                  for j in range(__lowerCamelCase					):
                        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(__lowerCamelCase      :    EvalPrediction					) -> Dict:
            lowercase_			,				lowercase_     			=   align_predictions(p.predictions			,		p.label_ids					)

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

      # Initialize our Trainer
      lowercase_     			=   TFTrainer(
          model=__lowerCamelCase			,		args=__lowerCamelCase			,		train_dataset=train_dataset.get_dataset() if train_dataset else None			,		eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,		compute_metrics=__lowerCamelCase			,		)

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

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

            lowercase_     			=   trainer.evaluate()
            lowercase_     			=   os.path.join(training_args.output_dir			,		"eval_results.txt"					)

            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:
            lowercase_     			=   TFTokenClassificationDataset(
                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			,		)

            lowercase_			,				lowercase_			,				lowercase_     			=   trainer.predict(test_dataset.get_dataset()					)
            lowercase_			,				lowercase_     			=   align_predictions(__lowerCamelCase			,		__lowerCamelCase					)
            lowercase_     			=   classification_report(__lowerCamelCase			,		__lowerCamelCase					)

            logger.info("\n%s"			,		__lowerCamelCase					)

            lowercase_     			=   os.path.join(training_args.output_dir			,		"test_results.txt"					)

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

            # Save predictions
            lowercase_     			=   os.path.join(training_args.output_dir			,		"test_predictions.txt"					)

            with open(__lowerCamelCase			,		"w"					) as writer:
                  with open(os.path.join(data_args.data_dir			,		"test.txt"					)			,		"r"					) as f:
                        lowercase_     			=   0

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

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    lowercase_     			=   line.split()[0] + " " + preds_list[example_id].pop(0					) + "\n"

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

      return results


if __name__ == "__main__":
       main()







'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






