

'''simple docstring'''

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


a       :      Tuple  	=			logging.get_logger(__name__)


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset

    from transformers import Trainer







    class   UpperCamelCase__   (       lowercase__       ):


      """simple docstring"""

      def __init__(			self   ,      snake_case = 1_0_1      ):


           '''simple docstring'''

           UpperCAmelCase		:							Tuple        =       length







      def __len__(			self      ):


           '''simple docstring'''

           return self.length







      def __getitem__(			self   ,      snake_case      ):


           '''simple docstring'''

           return i







    class   UpperCamelCase__   :


      """simple docstring"""

      def __call__(			self   ,      snake_case      ):


           '''simple docstring'''

           return {"input_ids": torch.tensor(snake_case      ), "labels": torch.tensor(snake_case      )}







    class   UpperCamelCase__   (       nn.Module       ):


      """simple docstring"""

      def __init__(			self      ):


           '''simple docstring'''

           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           UpperCAmelCase		:							Dict        =       nn.Linear(1_2_0   ,      8_0      )







      def 				A_	(			self   ,      snake_case   ,      snake_case=None      ):


           '''simple docstring'''

           if labels is not None:
                return torch.tensor(0.0   ,      device=input_ids.device      ), input_ids
           else:
                return input_ids





def 				lowercase (					):



     '''simple docstring'''







     UpperCAmelCase		:							Union[str, Any]        =       HfArgumentParser((TrainingArguments,)							)
     sys.argv += ["--output_dir", "./examples"]
     UpperCAmelCase		:							Optional[Any]        =       parser.parse_args_into_dataclasses()[0]

     logger.warning(
         F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
         F"tpu_num_cores: {training_args.tpu_num_cores}"			,   )

     # Essentially, what we want to verify in the distributed case is
     # that we get all samples back, in the right order.
     # (this is crucial for prediction for instance)
     for dataset_length in [1001, 256, 15]:
          UpperCAmelCase		:							Dict        =       DummyDataset(__magic_name__							)

          def compute_metrics(__magic_name__							) -> Dict:
               UpperCAmelCase		:							str        =       list(range(len(__magic_name__							)							)							)
               UpperCAmelCase		:							int        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
               return {"success": success}

          UpperCAmelCase		:							int        =       Trainer(
              model=DummyModel()			,   args=__magic_name__			,   data_collator=DummyDataCollator()			,   eval_dataset=__magic_name__			,   compute_metrics=__magic_name__			,   )
          UpperCAmelCase		:							int        =       trainer.evaluate()
          logger.info(__magic_name__							)
          if metrics["eval_success"] is not True:
               logger.error(__magic_name__							)
               exit(1							)

          UpperCAmelCase		:							str        =       trainer.predict(__magic_name__							)
          logger.info(p.metrics							)
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics							)
               exit(1							)

          UpperCAmelCase		:							Any        =       2

          UpperCAmelCase		:							Optional[int]        =       trainer.evaluate()
          logger.info(__magic_name__							)
          if metrics["eval_success"] is not True:
               logger.error(__magic_name__							)
               exit(1							)

          UpperCAmelCase		:							Dict        =       trainer.predict(__magic_name__							)
          logger.info(p.metrics							)
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics							)
               exit(1							)

          UpperCAmelCase		:							str        =       None

     logger.info("🔥 All distributed tests successful"							)





def 				lowercase (					__magic_name__							):



     '''simple docstring'''







     main()


if __name__ == "__main__":
    main()



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
