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 		_lowercase						(					snake_case_       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





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

  # DPR tok
  UpperCamelCase_					:						List[Any] =					[
      "[UNK]",
      "[CLS]",
      "[SEP]",
      "[PAD]",
      "[MASK]",
      "want",
      "##want",
      "##ed",
      "wa",
      "un",
      "runn",
      "##ing",
      ",",
      "low",
      "lowest",
  ]
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      'dpr_tokenizer'			)
  os.makedirs(__A   ,      exist_ok=__A			)
  UpperCamelCase_					:						str =					os.path.join(__A   ,      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
  UpperCamelCase_					:						str =					[
      "l",
      "o",
      "w",
      "e",
      "r",
      "s",
      "t",
      "i",
      "d",
      "n",
      "\u0120",
      "\u0120l",
      "\u0120n",
      "\u0120lo",
      "\u0120low",
      "er",
      "\u0120lowest",
      "\u0120newer",
      "\u0120wider",
      "<unk>",
  ]
  UpperCamelCase_					:						Union[str, Any] =					dict(zip(__A   ,      range(len(__A			)			)			)			)
  UpperCamelCase_					:						str =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
  UpperCamelCase_					:						Any =					{"unk_token": "<unk>"}

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      'bart_tokenizer'			)
  os.makedirs(__A   ,      exist_ok=__A			)
  UpperCamelCase_					:						Optional[Any] =					os.path.join(__A   ,      BART_VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Dict =					os.path.join(__A   ,      BART_VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'   ,      encoding='utf-8'			) as fp:
   fp.write(json.dumps(__A			) + '\n'			)
  with open(self.merges_file   ,      'w'   ,      encoding='utf-8'			) as fp:
   fp.write('\n'.join(__A			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						int:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						str:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						str:

  """simple docstring"""





  shutil.rmtree(self.tmpdirname			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.get_dummy_dataset()
  UpperCamelCase_					:						Dict =					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:
   UpperCamelCase_					:						Optional[int] =					dataset
   UpperCamelCase_					:						List[Any] =					RagRetriever(
       __A   ,      question_encoder_tokenizer=self.get_dpr_tokenizer()   ,      generator_tokenizer=self.get_bart_tokenizer()   ,      )
  return retriever

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					self.get_dummy_dataset()
  UpperCamelCase_					:						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:
   UpperCamelCase_					:						List[Any] =					os.path.join(self.tmpdirname   ,      'dataset'			)
   UpperCamelCase_					:						Dict =					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
   UpperCamelCase_					:						Union[str, Any] =					RagRetriever(
       __A   ,      question_encoder_tokenizer=self.get_dpr_tokenizer()   ,      generator_tokenizer=self.get_bart_tokenizer()   ,      )
  else:
   UpperCamelCase_					:						Tuple =					RagRetriever(
       __A   ,      question_encoder_tokenizer=self.get_dpr_tokenizer()   ,      generator_tokenizer=self.get_bart_tokenizer()   ,      index=CustomHFIndex(config.retrieval_vector_size   ,      __A			)   ,      )
  return retriever

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					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			)

  UpperCamelCase_					:						List[Any] =					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'			)			)

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

  UpperCamelCase_					:						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   ,      )
  UpperCamelCase_					:						Tuple =					RagRetriever(
      __A   ,      question_encoder_tokenizer=self.get_dpr_tokenizer()   ,      generator_tokenizer=self.get_bart_tokenizer()			)
  return retriever

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					1
  UpperCamelCase_					:						Any =					self.get_dummy_canonical_hf_index_retriever()
  UpperCamelCase_					:						Optional[int] =					np.array(
      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]   ,      dtype=np.floataa			)
  UpperCamelCase_					:						Optional[int] =					retriever.retrieve(__A   ,      n_docs=__A			)
  self.assertEqual(retrieved_doc_embeds.shape   ,      (2, n_docs, self.retrieval_vector_size)			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(sorted(doc_dicts[0]			)   ,      ['embeddings', 'id', 'text', 'title']			)
  self.assertEqual(len(doc_dicts[0]['id']			)   ,      __A			)
  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 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Optional[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					1
  UpperCamelCase_					:						Any =					self.get_dummy_custom_hf_index_retriever(from_disk=__A			)
  UpperCamelCase_					:						int =					np.array(
      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]   ,      dtype=np.floataa			)
  UpperCamelCase_					:						List[str] =					retriever.retrieve(__A   ,      n_docs=__A			)
  self.assertEqual(retrieved_doc_embeds.shape   ,      (2, n_docs, self.retrieval_vector_size)			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(sorted(doc_dicts[0]			)   ,      ['embeddings', 'id', 'text', 'title']			)
  self.assertEqual(len(doc_dicts[0]['id']			)   ,      __A			)
  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 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Optional[Any]:

  """simple docstring"""





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

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

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					1
  UpperCamelCase_					:						str =					self.get_dummy_custom_hf_index_retriever(from_disk=__A			)
  UpperCamelCase_					:						Dict =					np.array(
      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]   ,      dtype=np.floataa			)
  UpperCamelCase_					:						str =					retriever.retrieve(__A   ,      n_docs=__A			)
  self.assertEqual(retrieved_doc_embeds.shape   ,      (2, n_docs, self.retrieval_vector_size)			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(sorted(doc_dicts[0]			)   ,      ['embeddings', 'id', 'text', 'title']			)
  self.assertEqual(len(doc_dicts[0]['id']			)   ,      __A			)
  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 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						int:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					1
  UpperCamelCase_					:						str =					self.get_dummy_legacy_index_retriever()
  UpperCamelCase_					:						Tuple =					np.array(
      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]   ,      dtype=np.floataa			)
  UpperCamelCase_					:						Tuple =					retriever.retrieve(__A   ,      n_docs=__A			)
  self.assertEqual(retrieved_doc_embeds.shape   ,      (2, n_docs, self.retrieval_vector_size)			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(sorted(doc_dicts[0]			)   ,      ['text', 'title']			)
  self.assertEqual(len(doc_dicts[0]['text']			)   ,      __A			)
  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 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





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

 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						str:

  """simple docstring"""





  import torch

  UpperCamelCase_					:						str =					1
  UpperCamelCase_					:						List[str] =					self.get_dummy_canonical_hf_index_retriever()
  UpperCamelCase_					:						List[Any] =					[[5, 7], [1_0, 1_1]]
  UpperCamelCase_					:						List[str] =					np.array(
      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]   ,      dtype=np.floataa			)
  UpperCamelCase_					:						Union[str, Any] =					retriever(__A   ,      __A   ,      prefix=retriever.config.generator.prefix   ,      n_docs=__A			)
  UpperCamelCase_					:						Optional[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(__A   ,      __A			)
  self.assertIsInstance(__A   ,      __A			)
  self.assertIsInstance(__A   ,      np.ndarray			)

  UpperCamelCase_					:						Optional[Any] =					retriever(
      __A   ,      __A   ,      prefix=retriever.config.generator.prefix   ,      n_docs=__A   ,      return_tensors='pt'   ,      )
  UpperCamelCase_					:						Optional[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(__A   ,      torch.Tensor			)
  self.assertIsInstance(__A   ,      torch.Tensor			)
  self.assertIsInstance(__A   ,      torch.Tensor			)


 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.get_dpr_ctx_encoder_tokenizer()
  UpperCamelCase_					:						Tuple =					1
  UpperCamelCase_					:						List[Any] =					self.get_dummy_custom_hf_index_retriever(from_disk=__A			)
  retriever.set_ctx_encoder_tokenizer(__A			)

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

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







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     warnings.warn(
         "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use DPTImageProcessor instead."						,				__A						,				)
     super().__init__(*__A						,				**__A )



