"""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:
						SCREAMING_SNAKE_CASE						=    None

if is_ray_available():
						import ray  # noqa: E402 # isort:skip
						from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
						SCREAMING_SNAKE_CASE						=    None
						SCREAMING_SNAKE_CASE						=    None
						SCREAMING_SNAKE_CASE						=    None


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





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







@require_distributed_retrieval
class 			_lowerCamelCase      (__lowerCamelCase  ):







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				tempfile.mkdtemp()
					_lowercase			:     Union[str, Any]      					=				8

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

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







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					shutil.rmtree(self.tmpdirname    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				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 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							bool      ,       lowerCamelCase_     :							Optional[int]=1_2_3_4_5    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				self.get_dummy_dataset()
					_lowercase			:     List[Any]      					=				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:
						_lowercase			:     Tuple      					=				dataset
						_lowercase			:     Optional[Any]      					=				RagPyTorchDistributedRetriever(
						    lowerCamelCase_      ,       question_encoder_tokenizer=self.get_dpr_tokenizer()      ,       generator_tokenizer=self.get_bart_tokenizer()      ,       )
						if init_retrieval:
							retriever.init_retrieval(lowerCamelCase_    )
					return retriever







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							bool    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							bool      ,       lowerCamelCase_     :							bool      ,       lowerCamelCase_     :							str=1_2_3_4_5    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							bool      ,       lowerCamelCase_     :							bool    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Dict      ,       lowerCamelCase_     :							RagRetriever      ,       lowerCamelCase_     :							np.array      ,       lowerCamelCase_     :							int    ):

					"""simple docstring"""






					_lowercase					,   _lowercase					,   _lowercase			:     List[str]      					=				retriever.retrieve(lowerCamelCase_      ,       n_docs=lowerCamelCase_    )
					self.assertEqual(retrieved_doc_embeds.shape      ,       (2, n_docs, self.retrieval_vector_size)    )
					self.assertEqual(len(lowerCamelCase_    )      ,       2    )
					self.assertEqual(sorted(doc_dicts[0]    )      ,       ['embeddings', 'id', 'text', 'title']    )
					self.assertEqual(len(doc_dicts[0]['id']    )      ,       lowerCamelCase_    )
					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 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     Tuple      					=				1
					_lowercase			:     Any      					=				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=lowerCamelCase_    )      ,       lowerCamelCase_      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase			:     int      					=				1
					_lowercase			:     Optional[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=lowerCamelCase_      ,       from_disk=lowerCamelCase_    )      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				1
					_lowercase			:     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_pytorch_retriever(init_retrieval=lowerCamelCase_      ,       from_disk=lowerCamelCase_    )      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       )







				@require_ray
				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				1
					_lowercase			:     Union[str, Any]      					=				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=lowerCamelCase_    )      ,       lowerCamelCase_      ,       lowerCamelCase_    )
					ray.shutdown()







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

					"""simple docstring"""






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







				@require_ray
				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				1
					_lowercase			:     List[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=lowerCamelCase_      ,       from_disk=lowerCamelCase_    )      ,       lowerCamelCase_      ,       lowerCamelCase_    )
					ray.shutdown()




"""simple docstring"""







def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	if n == 0:
		return 1

	elif n % 2 == 1:
		return binary_exponentiation(__UpperCAmelCase  ,n - 1      ) * a

	else:
		_lowercase			:     Dict      					=				binary_exponentiation(__UpperCAmelCase  ,n / 2      )
		return b * b


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

						SCREAMING_SNAKE_CASE						=    binary_exponentiation(BASE, POWER)
						print(f"""{BASE}^({POWER}) : {RESULT}""")



