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)
__magic_name__								=logging.getLogger()

__magic_name__								=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class     _A   (    __UpperCamelCase							):






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "pytorch"	)		->      Union[str, Any]:





       '''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[int]:





       '''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	)		->      Tuple:





       '''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	)		->      Optional[int]:





       '''simple docstring'''





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







from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds - self.mean) * 1.0 / self.std
       return embeds






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds * self.std) + self.mean
       return embeds



