
"""simple docstring"""
lowercase		:						int =					{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowercase		:						Dict =					{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					True
					__a:						Dict		    =					[]

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															connected_verts += dfs(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )

					return [vert, *connected_verts]


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					__a:						str		    =					len(lowerCamelCase_ )
					__a:						Any		    =					graph_size * [False]
					__a:						List[Any]		    =					[]

					for i in range(lowerCamelCase_ ):
										if not visited[i]:
															__a:						Any		    =					dfs(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )
															components_list.append(lowerCamelCase_ )

					return components_list


if __name__ == "__main__":
			import doctest

			doctest.testmod()


"""simple docstring"""
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:
			lowercase		:						str =					None

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


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


					'''simple docstring'''
					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__a:						int		    =					unittest.skip("""test requires Datasets, Faiss, psutil""" )(lowerCamelCase_ )
					return test_case




@require_distributed_retrieval
class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					tempfile.mkdtemp()
												__a:						int		    =					8

												# DPR tok
												__a:						Any		    =					[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												__a:						str		    =					os.path.join(self.tmpdirname						, """dpr_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(snake_case__						, 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
												__a:						Any		    =					[
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__a:						List[str]		    =					dict(zip(snake_case__						, range(len(snake_case__    )    )    )    )
												__a:						List[str]		    =					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__a:						List[str]		    =					{"""unk_token""": """<unk>"""}

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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												shutil.rmtree(self.tmpdirname    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						int		    =					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						:			Any						, snake_case__						:			bool						, snake_case__						:			Dict=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						str		    =					self.get_dummy_dataset()
												__a:						int		    =					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:
																	__a:						List[str]		    =					dataset
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
																	if init_retrieval:
																						retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			bool    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool						, snake_case__						:			Union[str, Any]=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.get_dummy_dataset()
												__a:						Optional[Any]		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												if from_disk:
																	__a:						int		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						int		    =					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
																	__a:						str		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
												else:
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						Tuple		    =					self.get_dummy_dataset()
												__a:						Dict		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												__a:						Optional[Any]		    =					ray.remote(snake_case__    )
												__a:						Any		    =					[remote_cls.remote() for _ in range(1    )]
												if from_disk:
																	__a:						List[Any]		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						Any		    =					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
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex.load_from_disk(
																	        vector_size=config.retrieval_vector_size						, dataset_path=config.passages_path						, index_path=config.index_path						, )						, )
												else:
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval()
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			RagRetriever						, snake_case__						:			np.array						, snake_case__						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Optional[Any]		    =					retriever.retrieve(snake_case__						, n_docs=snake_case__    )
												self.assertEqual(retrieved_doc_embeds.shape						, (2, n_docs, self.retrieval_vector_size)    )
												self.assertEqual(len(snake_case__    )						, 2    )
												self.assertEqual(sorted(doc_dicts[0]    )						, ["""embeddings""", """id""", """text""", """title"""]    )
												self.assertEqual(len(doc_dicts[0]["""id"""]    )						, snake_case__    )
												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						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						Tuple		    =					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=snake_case__    )						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						Tuple		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						int		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							@require_ray
							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						str		    =					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=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						List[str]		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )
												with self.assertRaises(snake_case__    ):
																	self.distributed_retriever_check(
																	    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )
												ray.shutdown()






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






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						str		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()
