





'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if not (isinstance(lowerCAmelCase							,     lowerCAmelCase  ) and isinstance(lowerCAmelCase							,     lowerCAmelCase  )):
            raise ValueError("""longest_common_substring() takes two strings for inputs"""  )

      _lowerCAmelCase												=							len(lowerCAmelCase  )
      _lowerCAmelCase												=							len(lowerCAmelCase  )

      _lowerCAmelCase												=							[[0] * (texta_length + 1) for _ in range(texta_length + 1  )]
      _lowerCAmelCase												=							0
      _lowerCAmelCase												=							0

      for i in range(1							,     texta_length + 1  ):
            for j in range(1							,     texta_length + 1  ):
                  if texta[i - 1] == texta[j - 1]:
                        _lowerCAmelCase												=							1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              _lowerCAmelCase												=							i
                              _lowerCAmelCase												=							dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()













'''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__    :       Any              =logging.getLogger()

A__    :       int              =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			UpperCAmelCase					(      snake_case_       ):




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




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			int      ,      __snake_case      :			str = "pytorch"  )    ->				int:
            _lowerCAmelCase												=							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """output"""  )
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """data"""  )
            self._create_dummy_data(data_dir=__snake_case  )

            _lowerCAmelCase												=							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"""  )

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

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




      @require_torch_gpu
      def     lowercase__   (      self      :			Dict  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      def     lowercase__   (      self      :			int  )    ->				Dict:
            _lowerCAmelCase												=							self._run_finetune(gpus=2  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_gpu
      @require_ray
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							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      :			int  )    ->				List[str]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )






