


"""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       (   snake_case_			):







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					tempfile.mkdtemp()
       snake_case					:   Union[str, Any]         =					8

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

       snake_case					:   Tuple         =					os.path.join(self.tmpdirname		,			"bart_tokenizer"		)
       os.makedirs(UpperCamelCase__		,			exist_ok=UpperCamelCase__		)
       snake_case					:   Optional[Any]         =					os.path.join(UpperCamelCase__		,			BART_VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case					:   int         =					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 				lowerCamelCase	(			self		)			->	DPRQuestionEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	DPRContextEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	BartTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					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 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   int         =					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:
           snake_case					:   int         =					dataset
           snake_case					:   int         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       return retriever







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   Any         =					RagConfig(
           retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name="custom"		,			)
       if from_disk:
           snake_case					:   str         =					os.path.join(self.tmpdirname		,			"dataset"		)
           snake_case					:   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
           snake_case					:   Any         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       else:
           snake_case					:   str         =					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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					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		)

       snake_case					:   Dict         =					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"		)		)

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

       snake_case					:   List[Any]         =					RagConfig(
           retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name="legacy"		,			index_path=self.tmpdirname		,			)
       snake_case					:   Dict         =					RagRetriever(
           UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
       return retriever







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   str         =					1
       snake_case					:   Any         =					self.get_dummy_canonical_hf_index_retriever()
       snake_case					:   Dict         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					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 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[str]         =					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:
               snake_case					:   List[str]         =					self.get_dummy_dataset()
               retriever.save_pretrained(UpperCamelCase__		)
               snake_case					:   Union[str, Any]         =					RagRetriever.from_pretrained(UpperCamelCase__		)
               self.assertIsInstance(UpperCamelCase__		,			UpperCamelCase__		)
               snake_case					:   Union[str, Any]         =					np.array(
                   [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
               snake_case					:   List[Any]         =					retriever.retrieve(UpperCamelCase__		,			n_docs=1		)
               self.assertTrue(out is not None		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Dict         =					1
       snake_case					:   Union[str, Any]         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       snake_case					:   int         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					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 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					1
       snake_case					:   int         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       snake_case					:   int         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   List[str]         =					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 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					1
       snake_case					:   Tuple         =					self.get_dummy_legacy_index_retriever()
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					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 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       import torch

       snake_case					:   str         =					1
       snake_case					:   Dict         =					self.get_dummy_canonical_hf_index_retriever()
       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   Any         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   int         =					retriever(UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		)
       snake_case      ,snake_case      ,snake_case					:   Dict         =					(
           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		)

       snake_case					:   Tuple         =					retriever(
           UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		,			return_tensors="pt"		,			)
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   str         =					(  # 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 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					self.get_dpr_ctx_encoder_tokenizer()
       snake_case					:   Union[str, Any]         =					1
       snake_case					:   List[str]         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       retriever.set_ctx_encoder_tokenizer(UpperCamelCase__		)

       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   Dict         =					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"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





