



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)
lowerCamelCase       	=						logging.getLogger()

lowerCamelCase       	=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  Tuple   )			->						str:


             '''simple docstring'''




             os.makedirs(_UpperCAmelCase		,    exist_ok=_UpperCAmelCase   )
             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(_UpperCAmelCase		,    F"""{split}.{field}"""   )		,    "w"   ) as f:
                               f.write(_UpperCAmelCase   )



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  str = "pytorch"   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.get_auto_remove_tmp_dir()
             UpperCAmelCase_      			=							os.path.join(_UpperCAmelCase		,    "output"   )
             UpperCAmelCase_      			=							os.path.join(_UpperCAmelCase		,    "data"   )
             self._create_dummy_data(data_dir=_UpperCAmelCase   )

             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(_UpperCAmelCase		,    env=self.get_env()   )

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



       @require_torch_gpu
       def 					lowercase__    (							self      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._run_finetune(gpus=1   )
             self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,    0.2   )



       @require_torch_multi_gpu
       def 					lowercase__    (							self      :  str   )			->						str:


             '''simple docstring'''




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



       @require_torch_gpu
       @require_ray
       def 					lowercase__    (							self      :  Dict   )			->						Optional[int]:


             '''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 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, 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

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None
       UpperCamelCase						=					None



lowerCamelCase       	=						namedtuple("""CoinsDistribResult""", """moves excess""")
def        a__     (				lowerCAmelCase__				):

      if root is None:
            return 0

      # Validation
      def count_nodes(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left				) + count_nodes(node.right				) + 1

      def count_coins(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_coins(node.left				) + count_coins(node.right				) + node.data

      if count_nodes(lowerCAmelCase__				) != count_coins(lowerCAmelCase__				):
            raise ValueError("The nodes number should be same as the number of coins"				)

      # Main calculation
      def get_distrib(lowerCAmelCase__				) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0     ,      1				)

            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.left				)
            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.right				)

            UpperCAmelCase_      			=							1 - left_distrib_excess
            UpperCAmelCase_      			=							1 - right_distrib_excess

            UpperCAmelCase_      			=							(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowerCAmelCase__				)
                + abs(lowerCAmelCase__				)
            )
            UpperCAmelCase_      			=							node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowerCAmelCase__     ,      lowerCAmelCase__				)

      return get_distrib(lowerCAmelCase__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




