






"""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 						snake_case  (  UpperCAmelCase	):





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Dict    =       tempfile.mkdtemp()
      a					:      Tuple    =       8

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

      a					:      List[Any]    =       os.path.join(self.tmpdirname    ,		'bart_tokenizer'      )
      os.makedirs(A    ,		exist_ok=A      )
      a					:      List[Any]    =       os.path.join(A    ,		BART_VOCAB_FILES_NAMES['vocab_file']      )
      a					:      Tuple    =       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     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      shutil.rmtree(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      int    =       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     : Any      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.get_dummy_dataset()
      a					:      Any    =       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:
        a					:      str    =       dataset
        a					:      Optional[int]    =       RagRetriever(
            A    ,		question_encoder_tokenizer=self.get_dpr_tokenizer()    ,		generator_tokenizer=self.get_bart_tokenizer()    ,		)
      return retriever





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : bool      ):


      '''simple docstring'''




      a					:      Tuple    =       self.get_dummy_dataset()
      a					:      int    =       RagConfig(
          retrieval_vector_size=self.retrieval_vector_size    ,		question_encoder=DPRConfig().to_dict()    ,		generator=BartConfig().to_dict()    ,		index_name='custom'    ,		)
      if from_disk:
        a					:      Any    =       os.path.join(self.tmpdirname    ,		'dataset'      )
        a					:      str    =       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
        a					:      int    =       RagRetriever(
            A    ,		question_encoder_tokenizer=self.get_dpr_tokenizer()    ,		generator_tokenizer=self.get_bart_tokenizer()    ,		)
      else:
        a					:      Dict    =       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     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      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      )

      a					:      List[str]    =       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'      )      )

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

      a					:      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    ,		)
      a					:      List[str]    =       RagRetriever(
          A    ,		question_encoder_tokenizer=self.get_dpr_tokenizer()    ,		generator_tokenizer=self.get_bart_tokenizer()      )
      return retriever





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       1
      a					:      Tuple    =       self.get_dummy_canonical_hf_index_retriever()
      a					:      str    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      Dict    =       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     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Dict    =       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:
          a					:      Optional[int]    =       self.get_dummy_dataset()
          retriever.save_pretrained(A      )
          a					:      Dict    =       RagRetriever.from_pretrained(A      )
          self.assertIsInstance(A    ,		A      )
          a					:      Tuple    =       np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
          a					:      Optional[int]    =       retriever.retrieve(A    ,		n_docs=1      )
          self.assertTrue(out is not None      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      int    =       1
      a					:      List[Any]    =       self.get_dummy_custom_hf_index_retriever(from_disk=A      )
      a					:      str    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      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     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       1
      a					:      Optional[Any]    =       self.get_dummy_custom_hf_index_retriever(from_disk=A      )
      a					:      Optional[int]    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      Dict    =       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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Any    =       1
      a					:      Union[str, Any]    =       self.get_dummy_legacy_index_retriever()
      a					:      Optional[Any]    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      Optional[Any]    =       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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




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





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      import torch

      a					:      Tuple    =       1
      a					:      str    =       self.get_dummy_canonical_hf_index_retriever()
      a					:      List[str]    =       [[5, 7], [1_0, 1_1]]
      a					:      Tuple    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      Dict    =       retriever(A    ,		A    ,		prefix=retriever.config.generator.prefix    ,		n_docs=A      )
      a					:      Any    =       (
          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      )

      a					:      Union[str, Any]    =       retriever(
          A    ,		A    ,		prefix=retriever.config.generator.prefix    ,		n_docs=A    ,		return_tensors='pt'    ,		)
      a					:      Dict    =       (  # 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     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.get_dpr_ctx_encoder_tokenizer()
      a					:      int    =       1
      a					:      Any    =       self.get_dummy_custom_hf_index_retriever(from_disk=A      )
      retriever.set_ctx_encoder_tokenizer(A      )

      a					:      Any    =       [[5, 7], [1_0, 1_1]]
      a					:      List[str]    =       np.array(
          [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,		dtype=np.floataa      )
      a					:      Optional[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.












"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




