

"""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 					_lowercase (			self							:			str	):
    __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(UpperCAmelCase__,					exist_ok=UpperCAmelCase__	)
    __lowercase								=						os.path.join(UpperCAmelCase__,					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(UpperCAmelCase__,					range(len(UpperCAmelCase__	)	)	)	)
    __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(UpperCAmelCase__,					exist_ok=UpperCAmelCase__	)
    __lowercase								=						os.path.join(UpperCAmelCase__,					BART_VOCAB_FILES_NAMES["vocab_file"]	)
    __lowercase								=						os.path.join(UpperCAmelCase__,					BART_VOCAB_FILES_NAMES["merges_file"]	)
    with open(self.vocab_file,					"w",					encoding="utf-8"	) as fp:
       fp.write(json.dumps(UpperCAmelCase__	) + "\n"	)
    with open(self.merges_file,					"w",					encoding="utf-8"	) as fp:
       fp.write("\n".join(UpperCAmelCase__	)	)


 def 					_lowercase (			self							:			List[str]	):
    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,					"dpr_tokenizer"	)	)


 def 					_lowercase (			self							:			Tuple	):
    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,					"dpr_tokenizer"	)	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,					"bart_tokenizer"	)	)


 def 					_lowercase (			self							:			Any	):
    shutil.rmtree(self.tmpdirname	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __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 					_lowercase (			self							:			Dict	):
    __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(
           UpperCAmelCase__,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					)
    return retriever


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			bool	):
    __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(
           UpperCAmelCase__,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					)
    else:
       __lowercase								=						RagRetriever(
           UpperCAmelCase__,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					index=CustomHFIndex(config.retrieval_vector_size,					UpperCAmelCase__	),					)
    return retriever


 def 					_lowercase (			self							:			List[Any]	):
    __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(UpperCAmelCase__,					open(UpperCAmelCase__,					"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(
        UpperCAmelCase__,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer()	)
    return retriever


 def 					_lowercase (			self							:			List[str]	):
    __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(UpperCAmelCase__,					n_docs=UpperCAmelCase__	)
    self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)	)
    self.assertEqual(len(UpperCAmelCase__	),					2	)
    self.assertEqual(sorted(doc_dicts[0]	),					["embeddings", "id", "text", "title"]	)
    self.assertEqual(len(doc_dicts[0]["id"]	),					UpperCAmelCase__	)
    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 					_lowercase (			self							:			Union[str, Any]	):
    __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(UpperCAmelCase__	)
          __lowercase								=						RagRetriever.from_pretrained(UpperCAmelCase__	)
          self.assertIsInstance(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
          __lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=1	)
          self.assertTrue(out is not None	)


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						1
    __lowercase								=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__	)
    __lowercase								=						np.array(
        [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
    __lowercase		,__lowercase		,__lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=UpperCAmelCase__	)
    self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)	)
    self.assertEqual(len(UpperCAmelCase__	),					2	)
    self.assertEqual(sorted(doc_dicts[0]	),					["embeddings", "id", "text", "title"]	)
    self.assertEqual(len(doc_dicts[0]["id"]	),					UpperCAmelCase__	)
    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 					_lowercase (			self							:			str	):
    __lowercase								=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__	)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(UpperCAmelCase__	)
       __lowercase								=						RagRetriever.from_pretrained(UpperCAmelCase__	)
       self.assertIsInstance(UpperCAmelCase__,					UpperCAmelCase__	)
       __lowercase								=						np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
       __lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=1	)
       self.assertTrue(out is not None	)


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						1
    __lowercase								=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__	)
    __lowercase								=						np.array(
        [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
    __lowercase		,__lowercase		,__lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=UpperCAmelCase__	)
    self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)	)
    self.assertEqual(len(UpperCAmelCase__	),					2	)
    self.assertEqual(sorted(doc_dicts[0]	),					["embeddings", "id", "text", "title"]	)
    self.assertEqual(len(doc_dicts[0]["id"]	),					UpperCAmelCase__	)
    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 					_lowercase (			self							:			Any	):
    __lowercase								=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__	)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(UpperCAmelCase__	)
       __lowercase								=						RagRetriever.from_pretrained(UpperCAmelCase__	)
       self.assertIsInstance(UpperCAmelCase__,					UpperCAmelCase__	)
       __lowercase								=						np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
       __lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=1	)
       self.assertTrue(out is not None	)


 def 					_lowercase (			self							:			List[Any]	):
    __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(UpperCAmelCase__,					n_docs=UpperCAmelCase__	)
    self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)	)
    self.assertEqual(len(UpperCAmelCase__	),					2	)
    self.assertEqual(sorted(doc_dicts[0]	),					["text", "title"]	)
    self.assertEqual(len(doc_dicts[0]["text"]	),					UpperCAmelCase__	)
    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 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(UpperCAmelCase__	)
       __lowercase								=						RagRetriever.from_pretrained(UpperCAmelCase__	)
       self.assertIsInstance(UpperCAmelCase__,					UpperCAmelCase__	)
       __lowercase								=						np.array(
           [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
       __lowercase								=						retriever.retrieve(UpperCAmelCase__,					n_docs=1	)
       self.assertTrue(out is not None	)


 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					_lowercase (			self							:			Tuple	):
    import torch

    __lowercase								=						1
    __lowercase								=						self.get_dummy_canonical_hf_index_retriever()
    __lowercase								=						[[5, 7], [1_0, 1_1]]
    __lowercase								=						np.array(
        [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
    __lowercase								=						retriever(UpperCAmelCase__,					UpperCAmelCase__,					prefix=retriever.config.generator.prefix,					n_docs=UpperCAmelCase__	)
    __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(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertIsInstance(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertIsInstance(UpperCAmelCase__,					np.ndarray	)

    __lowercase								=						retriever(
        UpperCAmelCase__,					UpperCAmelCase__,					prefix=retriever.config.generator.prefix,					n_docs=UpperCAmelCase__,					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(UpperCAmelCase__,					torch.Tensor	)
    self.assertIsInstance(UpperCAmelCase__,					torch.Tensor	)
    self.assertIsInstance(UpperCAmelCase__,					torch.Tensor	)



 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						self.get_dpr_ctx_encoder_tokenizer()
    __lowercase								=						1
    __lowercase								=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__	)
    retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__	)

    __lowercase								=						[[5, 7], [1_0, 1_1]]
    __lowercase								=						np.array(
        [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],					dtype=np.floataa	)
    __lowercase								=						retriever(UpperCAmelCase__,					UpperCAmelCase__,					prefix=retriever.config.generator.prefix,					n_docs=UpperCAmelCase__	)

    self.assertEqual(
        len(UpperCAmelCase__	),					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")	),					UpperCAmelCase__	)  # check for doc token related keys in dictionary.







"""simple docstring"""





import baseaa




def   _A  (  UpperCamelCase_     :       str)	->						bytes:
   '''simple docstring'''


   return baseaa.baaencode(string.encode("utf-8"))




def   _A  (  UpperCamelCase_     :       bytes)	->						str:
   '''simple docstring'''


   return baseaa.baadecode(UpperCamelCase_).decode("utf-8")


if __name__ == "__main__":
   _a       =							'Hello World!'
   _a       =							baseaa_encode(test)
   print(encoded)

   _a       =							baseaa_decode(encoded)
   print(decoded)




