

'''simple docstring'''
import numpy as np

lowerCamelCase :      Dict					 =     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class 							A__    :





  def __init__(		self      :	Tuple )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =np.array(__a )





  def 						A  (		self      :	Union[str, Any]							,					_a      :	str )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =np.where(letter == self.SQUARE )
       _SCREAMING_SNAKE_CASE							    =np.concatenate([indexa + 1, indexa + 1] )
       return indexes





  def 						A  (		self      :	str							,					_a      :	int							,					_a      :	int )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.SQUARE[indexa - 1, indexa - 1]
       return letter





  def 						A  (		self      :	Optional[Any]							,					_a      :	str )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =message.lower()
       _SCREAMING_SNAKE_CASE							    =message.replace(' '							,					'' )
       _SCREAMING_SNAKE_CASE							    =message.replace('j'							,					'i' )

       _SCREAMING_SNAKE_CASE							    =np.empty((2, len(__a )) )
       for letter_index in range(len(__a ) ):
            _SCREAMING_SNAKE_CASE							    =self.letter_to_numbers(message[letter_index] )

            _SCREAMING_SNAKE_CASE							    =numbers[0]
            _SCREAMING_SNAKE_CASE							    =numbers[1]

       _SCREAMING_SNAKE_CASE							    =first_step.reshape(2 * len(__a ) )
       _SCREAMING_SNAKE_CASE							    =''
       for numbers_index in range(len(__a ) ):
            _SCREAMING_SNAKE_CASE							    =int(second_step[numbers_index * 2] )
            _SCREAMING_SNAKE_CASE							    =int(second_step[(numbers_index * 2) + 1] )
            _SCREAMING_SNAKE_CASE							    =self.numbers_to_letter(__a							,					__a )
            _SCREAMING_SNAKE_CASE							    =encoded_message + letter

       return encoded_message





  def 						A  (		self      :	Dict							,					_a      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =message.lower()
       message.replace(' '							,					'' )
       _SCREAMING_SNAKE_CASE							    =np.empty(2 * len(__a ) )
       for letter_index in range(len(__a ) ):
            _SCREAMING_SNAKE_CASE							    =self.letter_to_numbers(message[letter_index] )
            _SCREAMING_SNAKE_CASE							    =numbers[0]
            _SCREAMING_SNAKE_CASE							    =numbers[1]

       _SCREAMING_SNAKE_CASE							    =first_step.reshape((2, len(__a )) )
       _SCREAMING_SNAKE_CASE							    =''
       for numbers_index in range(len(__a ) ):
            _SCREAMING_SNAKE_CASE							    =int(second_step[0, numbers_index] )
            _SCREAMING_SNAKE_CASE							    =int(second_step[1, numbers_index] )
            _SCREAMING_SNAKE_CASE							    =self.numbers_to_letter(__a							,					__a )
            _SCREAMING_SNAKE_CASE							    =decoded_message + letter

       return decoded_message

'''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)
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger()

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        __A      ( UpperCamelCase__	):



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



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    str = "pytorch" ):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir()
         UpperCAmelCase_           =   os.path.join(__a   ,			"output" )
         UpperCAmelCase_           =   os.path.join(__a   ,			"data" )
         self._create_dummy_data(data_dir=__a )

         UpperCAmelCase_           =   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" )

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

         UpperCAmelCase_           =   os.path.join(__a   ,			"metrics.json" )
         with open(__a ) as f:
              UpperCAmelCase_           =   json.load(__a )
         return result



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



    @require_torch_multi_gpu
    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self._run_finetune(gpus=2 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_gpu
    @require_ray
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   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 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )




