




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__							:							Union[str, Any]													=     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__							:							Optional[int]													=     None
				lowercase__							:							List[str]													=     None




def 		A_	(							snake_case							:				int	) ->      List[str]:



						'''simple docstring'''

						if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
												__UpperCamelCase       					=     unittest.skip('''test requires Datasets, Faiss, psutil'''	)(snake_case	)
						return test_case






@require_distributed_retrieval
class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()
													__UpperCamelCase       					=     8

													# DPR tok
													__UpperCamelCase       					=     [
													    '''[UNK]''',
													    '''[CLS]''',
													    '''[SEP]''',
													    '''[PAD]''',
													    '''[MASK]''',
													    '''want''',
													    '''##want''',
													    '''##ed''',
													    '''wa''',
													    '''un''',
													    '''runn''',
													    '''##ing''',
													    ''',''',
													    '''low''',
													    '''lowest''',
													]
													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		'''dpr_tokenizer'''     )
													os.makedirs(SCREAMING_SNAKE_CASE_       ,		exist_ok=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     os.path.join(SCREAMING_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
													__UpperCamelCase       					=     [
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''\u0120''',
													    '''\u0120l''',
													    '''\u0120n''',
													    '''\u0120lo''',
													    '''\u0120low''',
													    '''er''',
													    '''\u0120lowest''',
													    '''\u0120newer''',
													    '''\u0120wider''',
													    '''<unk>''',
													]
													__UpperCamelCase       					=     dict(zip(SCREAMING_SNAKE_CASE_       ,		range(len(SCREAMING_SNAKE_CASE_     )     )     )     )
													__UpperCamelCase       					=     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
													__UpperCamelCase       					=     {'''unk_token''': '''<unk>'''}

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



							def       A__						(					self     )->				DPRQuestionEncoderTokenizer:





													'''simple docstring'''






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



							def       A__						(					self     )->				BartTokenizer:





													'''simple docstring'''






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



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     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       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=12345     )->				RagPyTorchDistributedRetriever:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_dummy_dataset()
													__UpperCamelCase       					=     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:
																			__UpperCamelCase       					=     dataset
																			__UpperCamelCase       					=     RagPyTorchDistributedRetriever(
																			    SCREAMING_SNAKE_CASE_       ,		question_encoder_tokenizer=self.get_dpr_tokenizer()       ,		generator_tokenizer=self.get_bart_tokenizer()       ,		)
																			if init_retrieval:
																									retriever.init_retrieval(SCREAMING_SNAKE_CASE_     )
													return retriever



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				RagRayDistributedRetriever:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=12345     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_dummy_dataset()
													__UpperCamelCase       					=     RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size       ,		question_encoder=DPRConfig().to_dict()       ,		generator=BartConfig().to_dict()       ,		index_name='''custom'''       ,		)
													if from_disk:
																			__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		'''dataset'''     )
																			__UpperCamelCase       					=     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
																			__UpperCamelCase       					=     RagPyTorchDistributedRetriever(
																			    SCREAMING_SNAKE_CASE_       ,		question_encoder_tokenizer=self.get_dpr_tokenizer()       ,		generator_tokenizer=self.get_bart_tokenizer()       ,		)
													else:
																			__UpperCamelCase       					=     RagPyTorchDistributedRetriever(
																			    SCREAMING_SNAKE_CASE_       ,		question_encoder_tokenizer=self.get_dpr_tokenizer()       ,		generator_tokenizer=self.get_bart_tokenizer()       ,		index=CustomHFIndex(config.retrieval_vector_size       ,		SCREAMING_SNAKE_CASE_     )       ,		)
													if init_retrieval:
																			retriever.init_retrieval(SCREAMING_SNAKE_CASE_     )
													return retriever



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     retriever.retrieve(SCREAMING_SNAKE_CASE_       ,		n_docs=SCREAMING_SNAKE_CASE_     )
													self.assertEqual(retrieved_doc_embeds.shape       ,		(2, n_docs, self.retrieval_vector_size)     )
													self.assertEqual(len(SCREAMING_SNAKE_CASE_     )       ,		2     )
													self.assertEqual(sorted(doc_dicts[0]     )       ,		['''embeddings''', '''id''', '''text''', '''title''']     )
													self.assertEqual(len(doc_dicts[0]['''id''']     )       ,		SCREAMING_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       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_       ,		from_disk=SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		)



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_       ,		from_disk=SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		)



							@require_ray
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													ray.shutdown()



							@require_ray
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






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



							@require_ray
							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_       ,		from_disk=SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													ray.shutdown()






def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Tuple	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						assert b != 0, "This cannot accept modulo that is == 0"
						if n == 0:
												return 1

						if n % 2 == 1:
												return (bin_exp_mod(snake_case	, n - 1	, snake_case	) * a) % b

						__UpperCamelCase       					=     bin_exp_mod(snake_case	, n / 2	, snake_case	)
						return (r * r) % b


if __name__ == "__main__":
				try:
								lowercase__							:							Dict													=     int(input("Enter Base : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Power : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Modulo : ").strip())
				except ValueError:
								print("Invalid literal for integer")

				print(bin_exp_mod(BASE, POWER, MODULO))




