





'''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     _a						(			__a				):
       def 					A   (			self		:			Any     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           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 					A   (			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			str = "pytorch"       ):



           '''simple docstring'''

           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"\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'''       )

           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 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self._run_finetune(gpus=1       )
           self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']     ,       0.2       )
       @require_torch_multi_gpu
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self._run_finetune(gpus=2       )
           self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']     ,       0.2       )
       @require_torch_gpu
       @require_ray
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           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 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self._run_finetune(gpus=1     ,       distributed_retriever='''ray'''       )
           self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']     ,       0.2       )










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




