





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)
_lowerCamelCase   				=			logging.getLogger()

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




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def      _snake_case  (							self    :Optional[int]							,				__A    :Union[str, Any]		)   ->   Dict:



									"""simple docstring"""



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


							def      _snake_case  (							self    :List[str]							,				__A    :int							,				__A    :str = "pytorch"		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				"""output"""		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				"""data"""		)
									self._create_dummy_data(data_dir=__A		)

									SCREAMING_SNAKE_CASE__			=      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"""		)

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

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


							@require_torch_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   List[str]:



									"""simple docstring"""



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


							@require_torch_multi_gpu
							def      _snake_case  (							self    :List[Any]		)   ->   str:



									"""simple docstring"""



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


							@require_torch_gpu
							@require_ray
							def      _snake_case  (							self    :int		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :int		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      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,
)


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

_lowerCamelCase   				=			'file'






@pytest.fixture(scope="""session"""    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / (FILE_PATH + """.zstd""")
		SCREAMING_SNAKE_CASE__			=      bytes(UpperCamelCase__					,   """utf-8"""    )
		with zstd.open(UpperCamelCase__					,   """wb"""    ) as f:
				f.write(UpperCamelCase__    )
		return path






@pytest.fixture
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		with open(os.path.join(tmpfs.local_root_dir					,   UpperCamelCase__    )					,   """w"""    ) as f:
				f.write(UpperCamelCase__    )
		return FILE_PATH






@pytest.mark.parametrize("""compression_format"""					,   ["""gzip""", """xz""", """zstd"""]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int					,   UpperCamelCase__:							Dict					,   UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
		SCREAMING_SNAKE_CASE__			=      input_paths[compression_format]
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      DownloadConfig(cache_dir=UpperCamelCase__					,   extract_compressed_file=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      cached_path(UpperCamelCase__					,   download_config=UpperCamelCase__    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      """custom_cache"""
		SCREAMING_SNAKE_CASE__			=      """custom_extracted_dir"""
		SCREAMING_SNAKE_CASE__			=      tmp_path / """custom_extracted_path"""
		if default_extracted:
				SCREAMING_SNAKE_CASE__			=      ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
		else:
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""					,   UpperCamelCase__    )
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""					,   str(UpperCamelCase__    )    )
				SCREAMING_SNAKE_CASE__			=      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[int]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve()    )
		assert cached_path(UpperCamelCase__    ) == text_file
		# relative path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve().relative_to(Path(os.getcwd()    )    )    )
		assert cached_path(UpperCamelCase__    ) == text_file






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(tmp_path.resolve() / """__missing_file__.txt"""    )
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )
		# relative path
		SCREAMING_SNAKE_CASE__			=      """./__missing_file__.txt"""
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      get_from_cache(f'''tmp://{tmpfs_file}'''    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		assert output_file_content == FILE_CONTENT






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       ):
		with pytest.raises(UpperCamelCase__    ):
				cached_path("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				http_get("""https://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				http_head("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				ftp_get("""ftp://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				ftp_head("""ftp://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				fsspec_get("""s3://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				fsspec_head("""s3://huggingface.co"""    )