





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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  int)			->List[str]:



   '''simple docstring'''



   os.makedirs(UpperCAmelCase_			,					exist_ok=UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         ={"source": "What is love ?", "target": "life"}
   lowerCamelCase__: str         ={"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
       lowerCamelCase__: Optional[int]         ="\n".join([contents[field]] * n_lines[split])
       with open(os.path.join(UpperCAmelCase_			,					F"""{split}.{field}""")			,					"w") as f:
         f.write(UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  str = "pytorch")			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.get_auto_remove_tmp_dir()
   lowerCamelCase__: Tuple         =os.path.join(UpperCAmelCase_			,					"output")
   lowerCamelCase__: str         =os.path.join(UpperCAmelCase_			,					"data")
   self._create_dummy_data(data_dir=UpperCAmelCase_)

   lowerCamelCase__: str         =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")

   lowerCamelCase__: List[Any]         =[sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
   execute_subprocess_async(UpperCAmelCase_			,					env=self.get_env())

   lowerCamelCase__: int         =os.path.join(UpperCAmelCase_			,					"metrics.json")
   with open(UpperCAmelCase_) as f:
     lowerCamelCase__: Optional[Any]         =json.load(UpperCAmelCase_)
   return result

 @require_torch_gpu
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[Any]:



   '''simple docstring'''



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

 @require_torch_multi_gpu
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self._run_finetune(gpus=2)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]			,					0.2)

 @require_torch_gpu
 @require_ray
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: str         =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 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Optional[int]:



   '''simple docstring'''



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


import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	huggingface_hub.__version__

       lowerCAmelCase_:							List[str]       =	"not installed"
       lowerCAmelCase_:							Dict       =	"NA"
       if is_torch_available():
          import torch

          lowerCAmelCase_:							List[str]       =	torch.__version__
          lowerCAmelCase_:							int       =	torch.cuda.is_available()

       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_transformers_available():
          import transformers

          lowerCAmelCase_:							Tuple       =	transformers.__version__

       lowerCAmelCase_:							Optional[int]       =	"not installed"
       if is_accelerate_available():
          import accelerate

          lowerCAmelCase_:							List[Any]       =	accelerate.__version__

       lowerCAmelCase_:							Dict       =	"not installed"
       if is_xformers_available():
          import xformers

          lowerCAmelCase_:							Dict       =	xformers.__version__

       lowerCAmelCase_:							Union[str, Any]       =	{
           "`diffusers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Huggingface_hub version": hub_version,
           "Transformers version": transformers_version,
           "Accelerate version": accelerate_version,
           "xFormers version": xformers_version,
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
       print(self.format_dict(lowerCamelCase__ ) )

       return info




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"