



"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase     =			logging.getLogger()

_UpperCAmelCase     =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 					a	(  UpperCAmelCase__       ):



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					List[str]		)     ->					Dict:


     '''simple docstring'''




     os.makedirs(lowerCAmelCase       ,		exist_ok=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={"""source""": """What is love ?""", """target""": """life"""}
     SCREAMING_SNAKE_CASE_: List[Any]     			={"""train""": 12, """val""": 2, """test""": 2}
     for split in ["train", "test", "val"]:
        for field in ["source", "target"]:
           SCREAMING_SNAKE_CASE_: Optional[Any]     			="""\n""".join([contents[field]] * n_lines[split]		)
           with open(os.path.join(lowerCAmelCase       ,		f'''{split}.{field}'''		)       ,		"""w"""		) as f:
              f.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					str = "pytorch"		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE_: Optional[int]     			=os.path.join(lowerCAmelCase       ,		"""output"""		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=os.path.join(lowerCAmelCase       ,		"""data"""		)
     self._create_dummy_data(data_dir=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

     if gpus > 0:
        testargs.append(f'''--gpus={gpus}'''		)
        if is_apex_available():
           testargs.append("""--fp16"""		)
     else:
        testargs.append("""--gpus=0"""		)
        testargs.append("""--distributed_backend=ddp_cpu"""		)
        testargs.append("""--num_processes=2"""		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=[sys.executable, str(Path(finetune_rag.__file__		).resolve()		)] + testargs
     execute_subprocess_async(lowerCAmelCase       ,		env=self.get_env()		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=os.path.join(lowerCAmelCase       ,		"""metrics.json"""		)
     with open(lowerCAmelCase		) as f:
        SCREAMING_SNAKE_CASE_: Any     			=json.load(lowerCAmelCase		)
     return result



  @require_torch_gpu
  def 	lowerCamelCase__       (						self :					Dict		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self._run_finetune(gpus=1		)
     self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)



  @require_torch_multi_gpu
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=self._run_finetune(gpus=2		)
     self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)



  @require_torch_gpu
  @require_ray
  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self._run_finetune(gpus=1       ,		distributed_retriever="""ray"""		)
     self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)





  @require_torch_multi_gpu
  @require_ray
  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self._run_finetune(gpus=1       ,		distributed_retriever="""ray"""		)
     self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)








"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

   # Display the confusion matrix of the classifier with both training and test sets
   ConfusionMatrixDisplay.from_estimator(
       lowercase							,			lowercase							,			lowercase							,			display_labels=lowercase							,			cmap="""Blues"""							,			normalize="""true"""							,			)
   plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



