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				( UpperCAmelCase__					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   List[str]   )    ->				Dict:
												os.makedirs(__A		,     exist_ok=__A   )
												_A								= {"""source""": """What is love ?""", """target""": """life"""}
												_A								= {"""train""": 12, """val""": 2, """test""": 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						_A								= """\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:   Optional[Any]		,     __A:   int		,     __A:   str = "pytorch"   )    ->				Any:
												_A								= self.get_auto_remove_tmp_dir()
												_A								= os.path.join(__A		,     '''output'''   )
												_A								= os.path.join(__A		,     '''data'''   )
												self._create_dummy_data(data_dir=__A   )

												_A								= 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								= [sys.executable, str(Path(finetune_rag.__file__   ).resolve()   )] + testargs
												execute_subprocess_async(__A		,     env=self.get_env()   )

												_A								= os.path.join(__A		,     '''metrics.json'''   )
												with open(__A   ) as f:
																	_A								= json.load(__A   )
												return result

							@require_torch_gpu
							def       __A (  self:   Dict   )    ->				Optional[int]:
												_A								= self._run_finetune(gpus=1   )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )

							@require_torch_multi_gpu
							def       __A (  self:   Union[str, Any]   )    ->				Union[str, Any]:
												_A								= self._run_finetune(gpus=2   )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )

							@require_torch_gpu
							@require_ray
							def       __A (  self:   List[str]   )    ->				Union[str, Any]:
												_A								= 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:   List[str]   )    ->				Any:
												_A								= self._run_finetune(gpus=1		,     distributed_retriever='''ray'''   )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
					_A								= bytes(_lowercase					,			'''utf-8''' )
					with zstd.open(_lowercase					,			'''wb''' ) as f:
										f.write(_lowercase )
					return path




@pytest.fixture
def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(os.path.join(tmpfs.local_root_dir					,			_lowercase )					,			'''w''' ) as f:
										f.write(_lowercase )
					return FILE_PATH




@pytest.mark.parametrize('''compression_format'''					,			['''gzip''', '''xz''', '''zstd'''] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
					_A								= input_paths[compression_format]
					_A								= tmp_path / '''cache'''
					_A								= DownloadConfig(cache_dir=_lowercase					,			extract_compressed_file=_lowercase )
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					with open(_lowercase ) as f:
										_A								= f.read()
					with open(_lowercase ) as f:
										_A								= f.read()
					assert extracted_file_content == expected_file_content




@pytest.mark.parametrize('''default_extracted'''					,			[True, False] )
@pytest.mark.parametrize('''default_cache_dir'''					,			[True, False] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= '''custom_cache'''
					_A								= '''custom_extracted_dir'''
					_A								= tmp_path / '''custom_extracted_path'''
					if default_extracted:
										_A								= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
					else:
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''					,			_lowercase )
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,			str(_lowercase ) )
										_A								= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					_A								= xz_file
					_A								= (
					    DownloadConfig(extract_compressed_file=_lowercase )
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,			extract_compressed_file=_lowercase )
					)
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					assert Path(_lowercase ).parent.parts[-2:] == expected




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(Path(_lowercase ).resolve() )
					assert cached_path(_lowercase ) == text_file
					# relative path
					_A								= str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
					assert cached_path(_lowercase ) == text_file




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(tmp_path.resolve() / '''__missing_file__.txt''' )
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )
					# relative path
					_A								= '''./__missing_file__.txt'''
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_from_cache(f"""tmp://{tmpfs_file}""" )
					with open(_lowercase ) as f:
										_A								= f.read()
					assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     ):

					'''simple docstring'''







					with pytest.raises(_lowercase ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										http_get('''https://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										http_head('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										ftp_get('''ftp://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										ftp_head('''ftp://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										fsspec_get('''s3://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										fsspec_head('''s3://huggingface.co''' )



