


"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case         =					{
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BigBirdPegasusForCausalLM""",
      """BigBirdPegasusForConditionalGeneration""",
      """BigBirdPegasusForQuestionAnswering""",
      """BigBirdPegasusForSequenceClassification""",
      """BigBirdPegasusModel""",
      """BigBirdPegasusPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  __snake_case         =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""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.





