
"""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:
					UpperCAmelCase__				:   List[Any]   							=			None

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

def lowercase_  (       _snake_case						):
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														SCREAMING_SNAKE_CASE__						:	Dict									=       unittest.skip("""test requires Datasets, Faiss, psutil"""						)(_snake_case						)
							return test_case



@require_distributed_retrieval
class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




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

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

											SCREAMING_SNAKE_CASE__						:	List[str]									=       os.path.join(self.tmpdirname       ,	"""bart_tokenizer"""   )
											os.makedirs(SCREAMING_SNAKE_CASE__       ,	exist_ok=SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       os.path.join(SCREAMING_SNAKE_CASE__       ,	BART_VOCAB_FILES_NAMES["""vocab_file"""]   )
											SCREAMING_SNAKE_CASE__						:	str									=       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      __magic_name__   (self   ) ->		DPRQuestionEncoderTokenizer:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		BartTokenizer:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname   )



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=1_23_45   ) ->		RagPyTorchDistributedRetriever:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.get_dummy_dataset()
											SCREAMING_SNAKE_CASE__						:	Tuple									=       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:
																		SCREAMING_SNAKE_CASE__						:	Any									=       dataset
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		RagRayDistributedRetriever:



											"""simple docstring"""




											ray.init(local_mode=SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       ,	question_encoder=DPRConfig().to_dict()       ,	generator=BartConfig().to_dict()       ,	)
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       ray.remote(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [remote_cls.remote() for _ in range(1   )]
											with patch("""transformers.models.rag.retrieval_rag.load_dataset"""   ) as mock_load_dataset:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.get_dummy_dataset()
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=1_23_45   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.get_dummy_dataset()
											SCREAMING_SNAKE_CASE__						:	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:
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       os.path.join(self.tmpdirname       ,	"""dataset"""   )
																		SCREAMING_SNAKE_CASE__						:	Optional[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
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       RagPyTorchDistributedRetriever(
																		    SCREAMING_SNAKE_CASE__       ,	question_encoder_tokenizer=self.get_dpr_tokenizer()       ,	generator_tokenizer=self.get_bart_tokenizer()       ,	)
											else:
																		SCREAMING_SNAKE_CASE__						:	str									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											ray.init(local_mode=SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.get_dummy_dataset()
											SCREAMING_SNAKE_CASE__						:	Dict									=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       ,	question_encoder=DPRConfig().to_dict()       ,	generator=BartConfig().to_dict()       ,	index_name="""custom"""       ,	)
											SCREAMING_SNAKE_CASE__						:	Any									=       ray.remote(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [remote_cls.remote() for _ in range(1   )]
											if from_disk:
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       os.path.join(self.tmpdirname       ,	"""dataset"""   )
																		SCREAMING_SNAKE_CASE__						:	Dict									=       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
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       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:
																		SCREAMING_SNAKE_CASE__						:	int									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		None:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       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      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       1
											SCREAMING_SNAKE_CASE__						:	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_pytorch_distributed_retriever(init_retrieval=SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       1
											SCREAMING_SNAKE_CASE__						:	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_pytorch_retriever(init_retrieval=SCREAMING_SNAKE_CASE__       ,	from_disk=SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       1
											SCREAMING_SNAKE_CASE__						:	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=SCREAMING_SNAKE_CASE__       ,	from_disk=SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	)



				@require_ray
				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       1
											SCREAMING_SNAKE_CASE__						:	Optional[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=SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											ray.shutdown()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       1
											SCREAMING_SNAKE_CASE__						:	Any									=       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      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       1
											SCREAMING_SNAKE_CASE__						:	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_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE__       ,	from_disk=SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											ray.shutdown()






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       len(_snake_case						)
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       []
														for j in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
																					for k in range(_snake_case						):
																												SCREAMING_SNAKE_CASE__						:	Optional[int]									=       val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(_snake_case						)
														matrix_c.append(_snake_case						)
							return matrix_c

def lowercase_  (       _snake_case						):
							return [[int(row == column						) for column in range(_snake_case						)] for row in range(_snake_case						)]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	Tuple									=       identity(2						)
							SCREAMING_SNAKE_CASE__						:	str									=       [[1, 1], [1, 0]]
							SCREAMING_SNAKE_CASE__						:	str									=       n - 1
							while n > 0:
														if n % 2 == 1:
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	int									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       int(n / 2						)
							return res_matrix[0][0]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	int									=       0
							SCREAMING_SNAKE_CASE__						:	str									=       1
							for _ in range(2       ,n + 1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fiba, fiba + fiba
							return fiba

def lowercase_  (       ):
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
														print(
														    f'''{ordinal} fibonacci number using matrix exponentiation is '''
														    f'''{nth_fibonacci_matrix(_snake_case						)} and using bruteforce is '''
														    f'''{nth_fibonacci_bruteforce(_snake_case						)}\n'''						)
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




