
'''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)
a_											=						logging.getLogger()

a_											=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class    UpperCAmelCase_				(	snake_case__					):





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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = "pytorch"):
     snake_case_ :    List[str]     			=						self.get_auto_remove_tmp_dir()
     snake_case_ :    List[Any]     			=						os.path.join(lowercase_ ,       "output")
     snake_case_ :    Tuple     			=						os.path.join(lowercase_ ,       "data")
     self._create_dummy_data(data_dir=lowercase_)

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

     snake_case_ :    str     			=						[sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
     execute_subprocess_async(lowercase_ ,       env=self.get_env())

     snake_case_ :    Union[str, Any]     			=						os.path.join(lowercase_ ,       "metrics.json")
     with open(lowercase_) as f:
         snake_case_ :    str     			=						json.load(lowercase_)
     return result





 @require_torch_gpu
 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						self._run_finetune(gpus=1)
     self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,       0.2)





 @require_torch_multi_gpu
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self._run_finetune(gpus=2)
     self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,       0.2)





 @require_torch_gpu
 @require_ray
 def        snake_case__		(    self):
     snake_case_ :    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        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self._run_finetune(gpus=1 ,       distributed_retriever="ray")
     self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,       0.2)






'''simple docstring'''
from __future__ import annotations

from cmath import sqrt

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."       )
    snake_case_ :    Union[str, Any]     			=						b * b - 4 * a * c

    snake_case_ :    List[str]     			=						(-b + sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)
    snake_case_ :    int     			=						(-b - sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_			, snake_case_ :    str     			=						quadratic_roots(a=5,		b=6,		c=1       )
    print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
  main()




