


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					:       Optional[int]			    =     logging.getLogger()

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



class A__     ( A__  ):



  """simple docstring"""



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

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  str = "pytorch"       ):
        a__	:  List[Any]     =   self.get_auto_remove_tmp_dir()
        a__	:  Dict     =   os.path.join(lowerCamelCase__				,				"output"       )
        a__	:  List[Any]     =   os.path.join(lowerCamelCase__				,				"data"       )
        self._create_dummy_data(data_dir=lowerCamelCase__       )

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

        a__	:  List[Any]     =   os.path.join(lowerCamelCase__				,				"metrics.json"       )
        with open(lowerCamelCase__       ) as f:
              a__	:  str     =   json.load(lowerCamelCase__       )
        return result

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

  @require_torch_multi_gpu
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Union[str, Any]     =   self._run_finetune(gpus=2       )
        self.assertGreaterEqual(result["test"][0]["test_avg_em"]				,				0.2       )

  @require_torch_gpu
  @require_ray
  def    _UpperCamelCase( self			:  List[str]       ):
        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    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  str     =   self._run_finetune(gpus=1				,				distributed_retriever="ray"       )
        self.assertGreaterEqual(result["test"][0]["test_avg_em"]				,				0.2       )





'''simple docstring'''






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase       							=				'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def    _snake_case  (	A					,       A=None			)		->       Optional[Any]:
							require_version(deps[pkg]					,       A			)