





'''simple docstring'''





import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
       import faiss





@require_faiss
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	int						    =							8

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

            _SCREAMING_SNAKE_CASE       :	int						    =							os.path.join(self.tmpdirname    ,						"bart_tokenizer"							)
            os.makedirs(__lowerCamelCase    ,						exist_ok=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							os.path.join(__lowerCamelCase    ,						BART_VOCAB_FILES_NAMES["vocab_file"]							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							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							)			->     DPRQuestionEncoderTokenizer:
            return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,						"dpr_tokenizer"							)							)




       def     UpperCamelCase_			(      self							)			->     DPRContextEncoderTokenizer:
            return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,						"dpr_tokenizer"							)							)




       def     UpperCamelCase_			(      self							)			->     BartTokenizer:
            return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,						"bart_tokenizer"							)							)




       def     UpperCamelCase_			(      self							)			->     Any:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, 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							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_dummy_dataset()
            _SCREAMING_SNAKE_CASE       :	Union[str, 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:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							dataset
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							RagRetriever(
                     __lowerCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						)
            return retriever




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     int:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_dummy_dataset()
            _SCREAMING_SNAKE_CASE       :	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:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							os.path.join(self.tmpdirname    ,						"dataset"							)
                 _SCREAMING_SNAKE_CASE       :	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
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							RagRetriever(
                     __lowerCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						)
            else:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							RagRetriever(
                     __lowerCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						index=CustomHFIndex(config.retrieval_vector_size    ,						__lowerCamelCase							)    ,						)
            return retriever




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							Dataset.from_dict(
                {
                    "id": ["0", "1"],
                    "text": ["foo", "bar"],
                    "title": ["Foo", "Bar"],
                    "embeddings": [np.ones(self.retrieval_vector_size + 1							), 2 * np.ones(self.retrieval_vector_size + 1							)],
                }							)
            dataset.add_faiss_index("embeddings"    ,						string_factory="Flat"    ,						metric_type=faiss.METRIC_INNER_PRODUCT							)

            _SCREAMING_SNAKE_CASE       :	int						    =							os.path.join(self.tmpdirname    ,						"hf_bert_base.hnswSQ8_correct_phi_128.c_index"							)
            dataset.save_faiss_index("embeddings"    ,						index_file_name + ".index.dpr"							)
            pickle.dump(dataset["id"]    ,						open(index_file_name + ".index_meta.dpr"    ,						"wb"							)							)

            _SCREAMING_SNAKE_CASE       :	str						    =							os.path.join(self.tmpdirname    ,						"psgs_w100.tsv.pkl"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
            pickle.dump(__lowerCamelCase    ,						open(__lowerCamelCase    ,						"wb"							)							)

            _SCREAMING_SNAKE_CASE       :	str						    =							RagConfig(
                retrieval_vector_size=self.retrieval_vector_size    ,						question_encoder=DPRConfig().to_dict()    ,						generator=BartConfig().to_dict()    ,						index_name="legacy"    ,						index_path=self.tmpdirname    ,						)
            _SCREAMING_SNAKE_CASE       :	Any						    =							RagRetriever(
                __lowerCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()							)
            return retriever




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							1
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_dummy_canonical_hf_index_retriever()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_dummy_canonical_hf_index_retriever()
            with tempfile.TemporaryDirectory() as tmp_dirname:
                 with patch("transformers.models.rag.retrieval_rag.load_dataset"							) as mock_load_dataset:
                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_dummy_dataset()
                      retriever.save_pretrained(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							RagRetriever.from_pretrained(__lowerCamelCase							)
                      self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							np.array(
                          [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							retriever.retrieve(__lowerCamelCase    ,						n_docs=1							)
                      self.assertTrue(out is not None							)




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							1
            _SCREAMING_SNAKE_CASE       :	str						    =							self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							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							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase							)
            with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							RagRetriever.from_pretrained(__lowerCamelCase							)
                 self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							np.array(
                     [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							retriever.retrieve(__lowerCamelCase    ,						n_docs=1							)
                 self.assertTrue(out is not None							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							1
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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							)			->     str:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase							)
            with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							RagRetriever.from_pretrained(__lowerCamelCase							)
                 self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							np.array(
                     [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							retriever.retrieve(__lowerCamelCase    ,						n_docs=1							)
                 self.assertTrue(out is not None							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	str						    =							1
            _SCREAMING_SNAKE_CASE       :	str						    =							self.get_dummy_legacy_index_retriever()
            _SCREAMING_SNAKE_CASE       :	Any						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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]							)    ,						["text", "title"]							)
            self.assertEqual(len(doc_dicts[0]["text"]							)    ,						__lowerCamelCase							)
            self.assertEqual(doc_dicts[0]["text"][0]    ,						"bar"							)  # max inner product is reached with second doc
            self.assertEqual(doc_dicts[1]["text"][0]    ,						"foo"							)  # max inner product is reached with first doc
            self.assertListEqual(doc_ids.tolist()    ,						[[1], [0]]							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.get_dummy_legacy_index_retriever()
            with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							RagRetriever.from_pretrained(__lowerCamelCase							)
                 self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							np.array(
                     [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							retriever.retrieve(__lowerCamelCase    ,						n_docs=1							)
                 self.assertTrue(out is not None							)




       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def     UpperCamelCase_			(      self							)			->     List[Any]:
            import torch

            _SCREAMING_SNAKE_CASE       :	str						    =							1
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_dummy_canonical_hf_index_retriever()
            _SCREAMING_SNAKE_CASE       :	int						    =							[[5, 7], [1_0, 1_1]]
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							retriever(__lowerCamelCase    ,						__lowerCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							(
                out["context_input_ids"],
                out["context_attention_mask"],
                out["retrieved_doc_embeds"],
            )
            self.assertEqual(retrieved_doc_embeds.shape    ,						(2, n_docs, self.retrieval_vector_size)							)
            self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
            self.assertIsInstance(__lowerCamelCase    ,						__lowerCamelCase							)
            self.assertIsInstance(__lowerCamelCase    ,						np.ndarray							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							retriever(
                __lowerCamelCase    ,						__lowerCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=__lowerCamelCase    ,						return_tensors="pt"    ,						)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							(  # noqa: F841
                out["context_input_ids"],
                out["context_attention_mask"],
                out["retrieved_doc_embeds"],
                out["doc_ids"],
            )
            self.assertEqual(retrieved_doc_embeds.shape    ,						(2, n_docs, self.retrieval_vector_size)							)
            self.assertIsInstance(__lowerCamelCase    ,						torch.Tensor							)
            self.assertIsInstance(__lowerCamelCase    ,						torch.Tensor							)
            self.assertIsInstance(__lowerCamelCase    ,						torch.Tensor							)







       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_dpr_ctx_encoder_tokenizer()
            _SCREAMING_SNAKE_CASE       :	str						    =							1
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase							)
            retriever.set_ctx_encoder_tokenizer(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[[5, 7], [1_0, 1_1]]
            _SCREAMING_SNAKE_CASE       :	Any						    =							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]    ,						dtype=np.floataa							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							retriever(__lowerCamelCase    ,						__lowerCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=__lowerCamelCase							)

            self.assertEqual(
                len(__lowerCamelCase							)    ,						6							)  # check whether the retriever output consist of 6 attributes including tokenized docs
            self.assertEqual(
                all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")							)    ,						__lowerCamelCase							)  # check for doc token related keys in dictionary.






def     lowerCamelCase__			(__lowerCamelCase = 10**9	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
     _SCREAMING_SNAKE_CASE       :	Any						    =							2

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0
     while perimeter <= max_perimeter:
          perimeters_sum += perimeter

          prev_value += 2 * value
          value += prev_value

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							2 * value + 2 if i % 2 == 0 else 2 * value - 2
          i += 1

     return perimeters_sum


if __name__ == "__main__":
       print(f"{solution() = }")