





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__       :			Tuple			      =						logging.getLogger()

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







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self					,			snake_case      )				->  Any:
      """simple docstring"""





      os.makedirs(snake_case					,			exist_ok=snake_case      )
      a__	:						Optional[int]      		=  {"source": "What is love ?", "target": "life"}
      a__	:						Union[str, Any]      		=  {"train": 12, "val": 2, "test": 2}
      for split in ["train", "test", "val"]:
         for field in ["source", "target"]:
            a__	:						List[Any]      		=  "\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 	_snake_case ( self					,			snake_case					,			snake_case = "pytorch"      )				->  str:
      """simple docstring"""





      a__	:						Any      		=  self.get_auto_remove_tmp_dir()
      a__	:						Tuple      		=  os.path.join(snake_case					,			"output"      )
      a__	:						List[str]      		=  os.path.join(snake_case					,			"data"      )
      self._create_dummy_data(data_dir=snake_case      )

      a__	:						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"      )

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

      a__	:						Tuple      		=  os.path.join(snake_case					,			"metrics.json"      )
      with open(snake_case      ) as f:
         a__	:						int      		=  json.load(snake_case      )
      return result





   @require_torch_gpu
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





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





   @require_torch_multi_gpu
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





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





   @require_torch_gpu
   @require_ray
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						int      		=  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      )				->  List[Any]:
      """simple docstring"""





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








SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						9.80665






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = g   ):

   if fluid_density <= 0:
      raise ValueError("Impossible fluid density"   )
   if volume < 0:
      raise ValueError("Impossible Object volume"   )
   if gravity <= 0:
      raise ValueError("Impossible Gravity"   )

   return fluid_density * gravity * volume


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

