
'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A = None			,   A = None			,   A = True			,   A = None			,   A = False			,   A = None			,   A = True			,   A = "arrow"			,   **A			,   ):
								super().__init__(
								    split=UpperCamelCase_			,   features=UpperCamelCase_			,   cache_dir=UpperCamelCase_			,   keep_in_memory=UpperCamelCase_			,   streaming=UpperCamelCase_			,   **UpperCamelCase_			,   )
								lowerCamelCase_					:      str     =		load_from_cache_file
								lowerCamelCase_					:      Dict     =		file_format
								lowerCamelCase_					:      Union[str, Any]     =		Spark(
								    df=UpperCamelCase_			,   features=UpperCamelCase_			,   cache_dir=UpperCamelCase_			,   working_dir=UpperCamelCase_			,   **UpperCamelCase_			,   )






			def        UpperCAmelCase__ (self      ):
								if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split      )
								lowerCamelCase_					:      Optional[int]     =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=UpperCamelCase_			,   file_format=self._file_format			,   )
								return self.builder.as_dataset(split=self.split      )


'''simple docstring'''


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)
__lowercase							:   Tuple					=     logging.getLogger()

__lowercase							:   str					=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 	__lowercase				(  _lowercase    ):

			def        UpperCAmelCase__ (self			,   A      ):
								os.makedirs(A			,   exist_ok=A      )
								lowerCamelCase_					:      Any     =		{'''source''': '''What is love ?''', '''target''': '''life'''}
								lowerCamelCase_					:      List[str]     =		{'''train''': 1_2, '''val''': 2, '''test''': 2}
								for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																		lowerCamelCase_					:      List[Any]     =		'''\n'''.join([contents[field]] * n_lines[split]      )
																		with open(os.path.join(A			,   F"""{split}.{field}"""      )			,   '''w'''      ) as f:
																							f.write(A      )

			def        UpperCAmelCase__ (self			,   A			,   A = "pytorch"      ):
								lowerCamelCase_					:      List[str]     =		self.get_auto_remove_tmp_dir()
								lowerCamelCase_					:      Tuple     =		os.path.join(A			,   '''output'''      )
								lowerCamelCase_					:      Union[str, Any]     =		os.path.join(A			,   '''data'''      )
								self._create_dummy_data(data_dir=A      )

								lowerCamelCase_					:      Tuple     =		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_					:      str     =		[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
								execute_subprocess_async(A			,   env=self.get_env()      )

								lowerCamelCase_					:      List[Any]     =		os.path.join(A			,   '''metrics.json'''      )
								with open(A      ) as f:
													lowerCamelCase_					:      List[str]     =		json.load(A      )
								return result

			@require_torch_gpu
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self._run_finetune(gpus=1      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )

			@require_torch_multi_gpu
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self._run_finetune(gpus=2      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )

			@require_torch_gpu
			@require_ray
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[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      ):
								lowerCamelCase_					:      Union[str, Any]     =		self._run_finetune(gpus=1			,   distributed_retriever='''ray'''      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )


