


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  						=    None

if is_ray_available():
						import ray  # noqa: E402 # isort:skip
						from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
						__UpperCamelCase  						=    None
						__UpperCamelCase  						=    None
						__UpperCamelCase  						=    None
def     UpperCamelCase_(			_A				:List[Any]					)->						Any:
			if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
						UpperCamelCase__   =    unittest.skip("test requires Datasets, Faiss, psutil"					)(_A					)
			return test_case







@require_distributed_retrieval
class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''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(snake_case ,  exist_ok=snake_case       )
						UpperCamelCase__   =    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
						UpperCamelCase__   =    [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__   =    dict(zip(snake_case ,  range(len(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(snake_case ,  exist_ok=snake_case       )
						UpperCamelCase__   =    os.path.join(snake_case ,  BART_VOCAB_FILES_NAMES["vocab_file"]       )
						UpperCamelCase__   =    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 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						shutil.rmtree(self.tmpdirname       )




			def 			snake_case__			(							self       ):

						'''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 			snake_case__			(							self ,  snake_case ,  snake_case=12345       ):

						'''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(
									    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 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						ray.init(local_mode=snake_case       )
						UpperCamelCase__   =    RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  )
						UpperCamelCase__   =    ray.remote(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(
									    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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case=12345       ):

						'''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(
									    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  )
						else:
									UpperCamelCase__   =    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 			snake_case__			(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						ray.init(local_mode=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(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(
									    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:
									UpperCamelCase__   =    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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    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 			snake_case__			(							self       ):

						'''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=snake_case       ) ,  snake_case ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''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=snake_case ,  from_disk=snake_case       ) ,  snake_case ,  snake_case ,  )




			def 			snake_case__			(							self       ):

						'''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=snake_case ,  from_disk=snake_case       ) ,  snake_case ,  snake_case ,  )




			@require_ray
			def 			snake_case__			(							self       ):

						'''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=snake_case       ) ,  snake_case ,  snake_case       )
						ray.shutdown()




			@require_ray
			def 			snake_case__			(							self       ):

						'''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(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 			snake_case__			(							self       ):

						'''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=snake_case ,  from_disk=snake_case       ) ,  snake_case ,  snake_case       )
						ray.shutdown()






import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			# sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# data position template + parity
			UpperCamelCase__   =    []
			# parity bit counter
			UpperCamelCase__   =    0
			# counter position of data bits
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / np.log(2					)).is_integer():
												data_out_gab.append("P"					)
												UpperCamelCase__   =    qtd_bp + 1
									else:
												data_out_gab.append("D"					)
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a given parity
						UpperCamelCase__   =    0
						# counter to control the loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			return data_out
def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0
			# list of parity received
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for x in range(1			,       len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]					)
						else:
									parity_received.append(data[cont_data]					)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			#  sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# Data position feedback + parity
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a certain parity
						UpperCamelCase__   =    0
						# Counter to control loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2					)					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # Parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			UpperCamelCase__   =    parity_received == parity
			return data_output, ack


