






'''simple docstring'''
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							int	,  a	:							List[str]=9_9	,  a	:							Optional[int]=1_3	,  a	:							Dict=1_6	,  a	:							Dict=7	,  a	:							List[Any]=True	,  a	:							str=True	,  a	:							Any=True	,  a	:							List[str]=False	,  a	:							Union[str, Any]=True	,  a	:							Any=2	,  a	:							Any=3_2	,  a	:							Dict=4	,  a	:							Tuple=4	,  a	:							Any=3_0	,  a	:							Optional[Any]=0	,  a	:							Dict=1	,  a	:							str=2	,  a	:							Optional[int]=None	,  ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  parent
       lowercase_						:     List[str]							     =  batch_size
       lowercase_						:     List[str]							     =  decoder_seq_length
       # For common tests
       lowercase_						:     Tuple							     =  self.decoder_seq_length
       lowercase_						:     Tuple							     =  is_training
       lowercase_						:     Union[str, Any]							     =  use_attention_mask
       lowercase_						:     Tuple							     =  use_labels

       lowercase_						:     Tuple							     =  vocab_size
       lowercase_						:     Any							     =  d_model
       lowercase_						:     Optional[Any]							     =  d_model
       lowercase_						:     Tuple							     =  decoder_layers
       lowercase_						:     Any							     =  decoder_layers
       lowercase_						:     Union[str, Any]							     =  decoder_ffn_dim
       lowercase_						:     List[str]							     =  decoder_attention_heads
       lowercase_						:     Optional[Any]							     =  decoder_attention_heads
       lowercase_						:     Optional[Any]							     =  eos_token_id
       lowercase_						:     int							     =  bos_token_id
       lowercase_						:     Optional[Any]							     =  pad_token_id
       lowercase_						:     Tuple							     =  decoder_start_token_id
       lowercase_						:     List[Any]							     =  use_cache
       lowercase_						:     Any							     =  max_position_embeddings

       lowercase_						:     Optional[int]							     =  None
       lowercase_						:     List[Any]							     =  decoder_seq_length
       lowercase_						:     List[str]							     =  2
       lowercase_						:     str							     =  1




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  ids_tensor([self.batch_size, self.decoder_seq_length]	,  self.vocab_size      )

       lowercase_						:     Dict							     =  None
       if self.use_attention_mask:
          lowercase_						:     Any							     =  ids_tensor([self.batch_size, self.decoder_seq_length]	,  vocab_size=2      )

       lowercase_						:     str							     =  None
       if self.use_labels:
          lowercase_						:     Tuple							     =  ids_tensor([self.batch_size, self.decoder_seq_length]	,  self.vocab_size      )

       lowercase_						:     int							     =  TrOCRConfig(
           vocab_size=self.vocab_size	,  d_model=self.d_model	,  decoder_layers=self.decoder_layers	,  decoder_ffn_dim=self.decoder_ffn_dim	,  decoder_attention_heads=self.decoder_attention_heads	,  eos_token_id=self.eos_token_id	,  bos_token_id=self.bos_token_id	,  use_cache=self.use_cache	,  pad_token_id=self.pad_token_id	,  decoder_start_token_id=self.decoder_start_token_id	,  max_position_embeddings=self.max_position_embeddings	,  )

       return (config, input_ids, attention_mask, lm_labels)




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Union[str, Any]	,  a	:							str	,  a	:							int	,  a	:							List[str]	,  ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  True
       lowercase_						:     Optional[int]							     =  TrOCRDecoder(config=a      ).to(a      ).eval()
       lowercase_						:     List[Any]							     =  input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       lowercase_						:     Optional[Any]							     =  model(a	,  use_cache=a      )
       lowercase_						:     int							     =  model(a      )
       lowercase_						:     Tuple							     =  model(a	,  use_cache=a      )

       self.parent.assertTrue(len(a      ) == len(a      )      )
       self.parent.assertTrue(len(a      ) == len(a      ) + 1      )

       lowercase_						:     List[str]							     =  outputs["past_key_values"]

       # create hypothetical next token and extent to next_input_ids
       lowercase_						:     Optional[Any]							     =  ids_tensor((2, 1)	,  config.vocab_size - 1      ) + 1

       # append to next input_ids and
       lowercase_						:     Optional[Any]							     =  torch.cat([input_ids, next_tokens]	,  dim=-1      )

       lowercase_						:     int							     =  model(a      )["last_hidden_state"]
       lowercase_						:     Any							     =  model(a	,  past_key_values=a      )["last_hidden_state"]

       # select random slice
       lowercase_						:     Tuple							     =  ids_tensor((1,)	,  output_from_past.shape[-1]      ).item()
       lowercase_						:     Union[str, Any]							     =  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       lowercase_						:     str							     =  output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(a	,  a	,  atol=1e-3      )





    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.prepare_config_and_inputs()
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     int							     =  config_and_inputs

       lowercase_						:     Union[str, Any]							     =  {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict


@require_torch
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Any									=     (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
    __lowerCamelCase: int									=     (TrOCRForCausalLM,) if is_torch_available() else ()
    __lowerCamelCase: List[str]									=     {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
    __lowerCamelCase: Optional[int]									=     True
    __lowerCamelCase: str									=     False




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  TrOCRStandaloneDecoderModelTester(self	,  is_training=a      )
       lowercase_						:     Dict							     =  ConfigTester(self	,  config_class=a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*a      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return





    @unittest.skip("The model doesn't support left padding"      )  # and it's not used enough to be worth fixing :)
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       pass












'''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 							_UpperCAmelCase    (		snake_case      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  tempfile.mkdtemp()
       lowercase_						:     str							     =  8

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

       lowercase_						:     Union[str, Any]							     =  os.path.join(self.tmpdirname	,  "bart_tokenizer"      )
       os.makedirs(a	,  exist_ok=a      )
       lowercase_						:     Union[str, Any]							     =  os.path.join(a	,  BART_VOCAB_FILES_NAMES["vocab_file"]      )
       lowercase_						:     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	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     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 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.get_dummy_dataset()
       lowercase_						:     Optional[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:
          lowercase_						:     Any							     =  dataset
          lowercase_						:     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]	,  a	:							bool      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.get_dummy_dataset()
       lowercase_						:     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:
          lowercase_						:     Optional[Any]							     =  os.path.join(self.tmpdirname	,  "dataset"      )
          lowercase_						:     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
          lowercase_						:     Tuple							     =  RagRetriever(
              a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()	,  )
       else:
          lowercase_						:     Union[str, Any]							     =  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[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, 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      )

       lowercase_						:     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"      )      )

       lowercase_						:     Dict							     =  os.path.join(self.tmpdirname	,  "psgs_w100.tsv.pkl"      )
       lowercase_						:     List[str]							     =  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
       pickle.dump(a	,  open(a	,  "wb"      )      )

       lowercase_						:     Optional[int]							     =  RagConfig(
           retrieval_vector_size=self.retrieval_vector_size	,  question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()	,  index_name="legacy"	,  index_path=self.tmpdirname	,  )
       lowercase_						:     str							     =  RagRetriever(
           a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()      )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     Tuple							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Any							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     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]      )	,  ["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	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     int							     =  self.get_dummy_legacy_index_retriever()
       lowercase_						:     Dict							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     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]      )	,  ["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	:							Optional[int]      ):






       '''simple docstring'''






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




    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       import torch

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

       lowercase_						:     Tuple							     =  retriever(
           a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a	,  return_tensors="pt"	,  )
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  (  # 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	:							Any      ):






       '''simple docstring'''






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

       lowercase_						:     Tuple							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     Optional[Any]							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     Dict							     =  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.




