"""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 					_A		(			lowerCAmelCase      ):

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      tempfile.mkdtemp()
            lowercase								=      8

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

            lowercase								=      os.path.join(self.tmpdirname						,			"""bart_tokenizer"""		)
            os.makedirs(__lowerCAmelCase						,			exist_ok=__lowerCAmelCase		)
            lowercase								=      os.path.join(__lowerCAmelCase						,			BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
            lowercase								=      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 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



            shutil.rmtree(self.tmpdirname		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      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		):







            """simple docstring"""



            lowercase								=      self.get_dummy_dataset()
            lowercase								=      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								=      dataset
                  lowercase								=      RagRetriever(
                      __lowerCAmelCase						,			question_encoder_tokenizer=self.get_dpr_tokenizer()						,			generator_tokenizer=self.get_bart_tokenizer()						,			)
            return retriever

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.get_dummy_dataset()
            lowercase								=      RagConfig(
                retrieval_vector_size=self.retrieval_vector_size						,			question_encoder=DPRConfig().to_dict()						,			generator=BartConfig().to_dict()						,			index_name="""custom"""						,			)
            if from_disk:
                  lowercase								=      os.path.join(self.tmpdirname						,			"""dataset"""		)
                  lowercase								=      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								=      RagRetriever(
                      __lowerCAmelCase						,			question_encoder_tokenizer=self.get_dpr_tokenizer()						,			generator_tokenizer=self.get_bart_tokenizer()						,			)
            else:
                  lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      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		)

            lowercase								=      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"""		)		)

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

            lowercase								=      RagConfig(
                retrieval_vector_size=self.retrieval_vector_size						,			question_encoder=DPRConfig().to_dict()						,			generator=BartConfig().to_dict()						,			index_name="""legacy"""						,			index_path=self.tmpdirname						,			)
            lowercase								=      RagRetriever(
                __lowerCAmelCase						,			question_encoder_tokenizer=self.get_dpr_tokenizer()						,			generator_tokenizer=self.get_bart_tokenizer()		)
            return retriever

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      1
            lowercase								=      self.get_dummy_canonical_hf_index_retriever()
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase				,				lowercase				,				lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      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:
                        lowercase								=      self.get_dummy_dataset()
                        retriever.save_pretrained(__lowerCAmelCase		)
                        lowercase								=      RagRetriever.from_pretrained(__lowerCAmelCase		)
                        self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)
                        lowercase								=      np.array(
                            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
                        lowercase								=      retriever.retrieve(__lowerCAmelCase						,			n_docs=1		)
                        self.assertTrue(out is not None		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      1
            lowercase								=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCAmelCase		)
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase				,				lowercase				,				lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCAmelCase		)
            with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(__lowerCAmelCase		)
                  lowercase								=      RagRetriever.from_pretrained(__lowerCAmelCase		)
                  self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      np.array(
                      [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
                  lowercase								=      retriever.retrieve(__lowerCAmelCase						,			n_docs=1		)
                  self.assertTrue(out is not None		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      1
            lowercase								=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCAmelCase		)
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase				,				lowercase				,				lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCAmelCase		)
            with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(__lowerCAmelCase		)
                  lowercase								=      RagRetriever.from_pretrained(__lowerCAmelCase		)
                  self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      np.array(
                      [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
                  lowercase								=      retriever.retrieve(__lowerCAmelCase						,			n_docs=1		)
                  self.assertTrue(out is not None		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      1
            lowercase								=      self.get_dummy_legacy_index_retriever()
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase				,				lowercase				,				lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_dummy_legacy_index_retriever()
            with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(__lowerCAmelCase		)
                  lowercase								=      RagRetriever.from_pretrained(__lowerCAmelCase		)
                  self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      np.array(
                      [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
                  lowercase								=      retriever.retrieve(__lowerCAmelCase						,			n_docs=1		)
                  self.assertTrue(out is not None		)

      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def 							A__			(		self		):







            """simple docstring"""



            import torch

            lowercase								=      1
            lowercase								=      self.get_dummy_canonical_hf_index_retriever()
            lowercase								=      [[5, 7], [10, 11]]
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase								=      retriever(__lowerCAmelCase						,			__lowerCAmelCase						,			prefix=retriever.config.generator.prefix						,			n_docs=__lowerCAmelCase		)
            lowercase				,				lowercase				,				lowercase								=      (
                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		)

            lowercase								=      retriever(
                __lowerCAmelCase						,			__lowerCAmelCase						,			prefix=retriever.config.generator.prefix						,			n_docs=__lowerCAmelCase						,			return_tensors="""pt"""						,			)
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      (  # 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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_dpr_ctx_encoder_tokenizer()
            lowercase								=      1
            lowercase								=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCAmelCase		)
            retriever.set_ctx_encoder_tokenizer(__lowerCAmelCase		)

            lowercase								=      [[5, 7], [10, 11]]
            lowercase								=      np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,			dtype=np.floataa		)
            lowercase								=      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.




"""simple docstring"""






def      UpperCAmelCase__			(							lowerCAmelCase__	:int = 1_0**9						)					->       int:
      '''simple docstring'''


      lowercase								=      1
      lowercase								=      2

      lowercase								=      0
      lowercase								=      0
      lowercase								=      0
      while perimeter <= max_perimeter:
            perimeters_sum += perimeter

            prev_value += 2 * value
            value += prev_value

            lowercase								=      2 * value + 2 if i % 2 == 0 else 2 * value - 2
            i += 1

      return perimeters_sum


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



