
"""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	(   __SCREAMING_SNAKE_CASE    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= tempfile.mkdtemp()
      __UpperCamelCase       						= 8

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

      __UpperCamelCase       						= os.path.join(self.tmpdirname			,  'bart_tokenizer'   )
      os.makedirs(__UpperCAmelCase			,  exist_ok=__UpperCAmelCase   )
      __UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  BART_VOCAB_FILES_NAMES['vocab_file']   )
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_dummy_dataset()
      __UpperCamelCase       						= 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       						= dataset
       __UpperCamelCase       						= RagRetriever(
           __UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
      return retriever





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_dummy_dataset()
      __UpperCamelCase       						= RagConfig(
          retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name='custom'			,  )
      if from_disk:
       __UpperCamelCase       						= os.path.join(self.tmpdirname			,  'dataset'   )
       __UpperCamelCase       						= 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       						= RagRetriever(
           __UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
      else:
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= 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       						= RagRetriever(
          __UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()   )
      return retriever





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 1
      __UpperCamelCase       						= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase   )
      __UpperCamelCase       						= np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,  dtype=np.floataa   )
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 1
      __UpperCamelCase       						= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase   )
      __UpperCamelCase       						= np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,  dtype=np.floataa   )
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      '''simple docstring'''




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





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

      '''simple docstring'''




      import torch

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

      __UpperCamelCase       						= retriever(
          __UpperCAmelCase			,  __UpperCAmelCase			,  prefix=retriever.config.generator.prefix			,  n_docs=__UpperCAmelCase			,  return_tensors='pt'			,  )
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= (  # 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_dpr_ctx_encoder_tokenizer()
      __UpperCamelCase       						= 1
      __UpperCamelCase       						= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase   )
      retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase   )

      __UpperCamelCase       						= [[5, 7], [10, 11]]
      __UpperCamelCase       						= np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,  dtype=np.floataa   )
      __UpperCamelCase       						= 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       Dict									=				{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[Any]									=				[
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      UpperCamelCase			:       Any									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



