
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)
UpperCamelCase_   	=	logging.getLogger()

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

class       a		(   __UpperCAmelCase					):







       def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  List[str] ):
        """simple docstring"""







        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       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int      ,				snake_case__  :  str = "pytorch" ):
        """simple docstring"""







        __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       UpperCAmelCase__					(						self  :  Any ):
        """simple docstring"""







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







       @require_torch_multi_gpu
       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







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







       @require_torch_gpu
       @require_ray
       def       UpperCAmelCase__					(						self  :  Any ):
        """simple docstring"""







        __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       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







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








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






