




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__A						:		Any          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				return TestCommand(
				    args.dataset						,			args.name						,			args.cache_dir						,			args.data_dir						,			args.all_configs						,			args.save_info or args.save_infos						,			args.ignore_verifications						,			args.force_redownload						,			args.clear_cache						,			)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= False  # to tell pytest it's not a test class




					@staticmethod
					def     _lowercase						(   _lowercase:		ArgumentParser):


									'''simple docstring'''


									__lowerCAmelCase					=  parser.add_parser("""test""",     help="""Test dataset implementation.""")
									test_parser.add_argument("""--name""",     type=_lowercase,     default=_lowercase,     help="""Dataset processing name""")
									test_parser.add_argument(
									    """--cache_dir""",     type=_lowercase,     default=_lowercase,     help="""Cache directory where the datasets are stored.""",     )
									test_parser.add_argument(
									    """--data_dir""",     type=_lowercase,     default=_lowercase,     help="""Can be used to specify a manual directory to get the files from.""",     )
									test_parser.add_argument("""--all_configs""",     action="""store_true""",     help="""Test all dataset configurations""")
									test_parser.add_argument(
									    """--save_info""",     action="""store_true""",     help="""Save the dataset infos in the dataset card (README.md)""")
									test_parser.add_argument(
									    """--ignore_verifications""",     action="""store_true""",     help="""Run the test without checksums and splits checks.""",     )
									test_parser.add_argument("""--force_redownload""",     action="""store_true""",     help="""Force dataset redownload""")
									test_parser.add_argument(
									    """--clear_cache""",     action="""store_true""",     help="""Remove downloaded files and cached datasets after each config test""",     )
									# aliases
									test_parser.add_argument("""--save_infos""",     action="""store_true""",     help="""alias to save_info""")
									test_parser.add_argument("""dataset""",     type=_lowercase,     help="""Name of the dataset to download""")
									test_parser.set_defaults(func=_test_command_factory)




					def __init__(   self:		Optional[Any],     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		str,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		bool,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  dataset
									__lowerCAmelCase					=  name
									__lowerCAmelCase					=  cache_dir
									__lowerCAmelCase					=  data_dir
									__lowerCAmelCase					=  all_configs
									__lowerCAmelCase					=  save_infos
									__lowerCAmelCase					=  ignore_verifications
									__lowerCAmelCase					=  force_redownload
									__lowerCAmelCase					=  clear_cache
									if clear_cache and not cache_dir:
													print(
													    """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
													    """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
													    """Please provide a --cache_dir that will be used to test the dataset script.""")
													exit(1)
									if save_infos:
													__lowerCAmelCase					=  True







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									fl_logger().setLevel(_lowercase)
									if self._name is not None and self._all_configs:
													print("""Both parameters `config` and `all_configs` can't be used at once.""")
													exit(1)
									__lowerCAmelCase	,				__lowerCAmelCase					=  self._dataset, self._name
									__lowerCAmelCase					=  dataset_module_factory(_lowercase)
									__lowerCAmelCase					=  import_main_class(module.module_path)
									__lowerCAmelCase					=  len(builder_cls.BUILDER_CONFIGS) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

									def get_builders() -> Generator[DatasetBuilder, None, None]:
													if self._all_configs and builder_cls.BUILDER_CONFIGS:
																	for i, config in enumerate(builder_cls.BUILDER_CONFIGS):
																					if "config_name" in module.builder_kwargs:
																									yield builder_cls(
																									    cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
																					else:
																									yield builder_cls(
																									    config_name=config.name,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )
													else:
																	if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs)
																	else:
																					yield builder_cls(
																					    config_name=_lowercase,     cache_dir=self._cache_dir,     data_dir=self._data_dir,     **module.builder_kwargs,     )

									for j, builder in enumerate(get_builders()):
													print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})''')
													__lowerCAmelCase					=  os.path.exists(
													    os.path.join(builder.get_imported_module_dir(),     datasets.config.DATASETDICT_INFOS_FILENAME))  # record checksums only if we need to update a (deprecated) dataset_infos.json
													builder.download_and_prepare(
													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
													    if not self._force_redownload
													    else DownloadMode.FORCE_REDOWNLOAD,     verification_mode=VerificationMode.NO_CHECKS
													    if self._ignore_verifications
													    else VerificationMode.ALL_CHECKS,     try_from_hf_gcs=_lowercase,     )
													builder.as_dataset()
													if self._save_infos:
																	builder._save_infos()

													# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
													# The dataset_infos are saved in the YAML part of the README.md

													# Let's move it to the original directory of the dataset script, to allow the user to
													# upload them on S3 at the same time afterwards.
													if self._save_infos:
																	__lowerCAmelCase					=  os.path.join(builder_cls.get_imported_module_dir(),     """README.md""")
																	__lowerCAmelCase					=  Path(_lowercase).name + """.py"""
																	__lowerCAmelCase					=  os.path.join(_lowercase,     _lowercase)
																	if os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  os.path.dirname(_lowercase)
																	elif os.path.isfile(_lowercase):
																					__lowerCAmelCase					=  path
																	elif os.path.isdir(_lowercase):  # for local directories containing only data files
																					__lowerCAmelCase					=  path
																	else:  # in case of a remote dataset
																					__lowerCAmelCase					=  None
																					print(f'''Dataset card saved at {dataset_readme_path}''')

																	# Move dataset_info back to the user
																	if dataset_dir is not None:
																					__lowerCAmelCase					=  os.path.join(_lowercase,     """README.md""")
																					copyfile(_lowercase,     _lowercase)
																					print(f'''Dataset card saved at {user_dataset_readme_path}''')

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
													if self._clear_cache:
																	if os.path.isdir(builder._cache_dir):
																					logger.warning(f'''Clearing cache at {builder._cache_dir}''')
																					rmtree(builder._cache_dir)
																	__lowerCAmelCase					=  os.path.join(self._cache_dir,     datasets.config.DOWNLOADED_DATASETS_DIR)
																	if os.path.isdir(_lowercase):
																					logger.warning(f'''Clearing cache at {download_dir}''')
																					rmtree(_lowercase)

									print("""Test successful.""")











import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	__A						:		str          =						None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	__A						:		int          =						None
	__A						:		Tuple          =						None
	__A						:		Union[str, Any]          =						None





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								__lowerCAmelCase					=  unittest.skip("""test requires Datasets, Faiss, psutil"""   )(UpperCamelCase__   )
				return test_case

@require_distributed_retrieval
class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  tempfile.mkdtemp()
									__lowerCAmelCase					=  8

									# DPR tok
									__lowerCAmelCase					=  [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dpr_tokenizer""")
									os.makedirs(_lowercase,     exist_ok=_lowercase)
									__lowerCAmelCase					=  os.path.join(_lowercase,     DPR_VOCAB_FILES_NAMES["""vocab_file"""])
									with open(self.vocab_file,     """w""",     encoding="""utf-8""") as vocab_writer:
													vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

									# BART tok
									__lowerCAmelCase					=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									__lowerCAmelCase					=  dict(zip(_lowercase,     range(len(_lowercase))))
									__lowerCAmelCase					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									__lowerCAmelCase					=  {"""unk_token""": """<unk>"""}

									__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """bart_tokenizer""")
									os.makedirs(_lowercase,     exist_ok=_lowercase)
									__lowerCAmelCase					=  os.path.join(_lowercase,     BART_VOCAB_FILES_NAMES["""vocab_file"""])
									__lowerCAmelCase					=  os.path.join(_lowercase,     BART_VOCAB_FILES_NAMES["""merges_file"""])
									with open(self.vocab_file,     """w""",     encoding="""utf-8""") as fp:
													fp.write(json.dumps(_lowercase) + """\n""")
									with open(self.merges_file,     """w""",     encoding="""utf-8""") as fp:
													fp.write("""\n""".join(_lowercase))




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """dpr_tokenizer"""))




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """bart_tokenizer"""))




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									shutil.rmtree(self.tmpdirname)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)],
									    })
									dataset.add_faiss_index("""embeddings""",     string_factory="""Flat""",     metric_type=faiss.METRIC_INNER_PRODUCT)
									return dataset




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool,     _lowercase:		Any=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
													if init_retrieval:
																	retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  self.get_dummy_dataset()
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     )
													if init_retrieval:
																	retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		int=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
									else:
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		List[Any],     _lowercase:		bool,     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex.load_from_disk(
													        vector_size=config.retrieval_vector_size,     dataset_path=config.passages_path,     index_path=config.index_path,     ),     )
									else:
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		int,     _lowercase:		RagRetriever,     _lowercase:		np.array,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  retriever.retrieve(_lowercase,     n_docs=_lowercase)
									self.assertEqual(retrieved_doc_embeds.shape,     (2, n_docs, self.retrieval_vector_size))
									self.assertEqual(len(_lowercase),     2)
									self.assertEqual(sorted(doc_dicts[0]),     ["""embeddings""", """id""", """text""", """title"""])
									self.assertEqual(len(doc_dicts[0]["""id"""]),     _lowercase)
									self.assertEqual(doc_dicts[0]["""id"""][0],     """1""")  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0],     """0""")  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist(),     [[1], [0]])




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					@require_ray
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_ray_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()




					@require_ray
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)
									with self.assertRaises(_lowercase):
													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )
									ray.shutdown()







					@require_ray
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()





