
import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
 raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
 raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
__lowerCAmelCase					:			Optional[int]			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			str			      =		"The Nymphenburg Palace is a beautiful palace in Munich!"


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    __lowercase		:			Optional[Any]	    =       {
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 1_024,
        '''hidden_size''': 768,
        '''max_length''': 512,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 1_024,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1E-5,
        '''token_type_vocab_size''': 2,
    }

    __lowercase		:			List[Any]	    =       bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    __lowercase		:			Union[str, Any]	    =       BERTEncoder(
        attention_cell=predefined_args['''attention_cell''']	,					num_layers=predefined_args['''num_layers''']	,					units=predefined_args['''units''']	,					hidden_size=predefined_args['''hidden_size''']	,					max_length=predefined_args['''max_length''']	,					num_heads=predefined_args['''num_heads''']	,					scaled=predefined_args['''scaled''']	,					dropout=predefined_args['''dropout''']	,					output_attention=__lowerCAmelCase	,					output_all_encodings=__lowerCAmelCase	,					use_residual=predefined_args['''use_residual''']	,					activation=predefined_args.get('''activation'''	,					'''gelu'''	)	,					layer_norm_eps=predefined_args.get('''layer_norm_eps'''	,					__lowerCAmelCase	)	,					)

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    __lowercase		:			int	    =       '''openwebtext_ccnews_stories_books_cased'''

    # Specify download folder to Gluonnlp's vocab
    __lowercase		:			Optional[Any]	    =       os.path.join(get_home_dir()	,					'''models'''	)
    __lowercase		:			List[Any]	    =       _load_vocab(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					cls=__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       nlp.model.BERTModel(
        __lowerCAmelCase	,					len(__lowerCAmelCase	)	,					units=predefined_args['''units''']	,					embed_size=predefined_args['''embed_size''']	,					embed_dropout=predefined_args['''embed_dropout''']	,					word_embed=predefined_args['''word_embed''']	,					use_pooler=__lowerCAmelCase	,					use_token_type_embed=__lowerCAmelCase	,					token_type_vocab_size=predefined_args['''token_type_vocab_size''']	,					use_classifier=__lowerCAmelCase	,					use_decoder=__lowerCAmelCase	,					)

    original_bort.load_parameters(__lowerCAmelCase	,					cast_dtype=__lowerCAmelCase	,					ignore_extra=__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       original_bort._collect_params_with_prefix()

    # Build our config 🤗
    __lowercase		:			Optional[int]	    =       {
        '''architectures''': ['''BertForMaskedLM'''],
        '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_act''': '''gelu''',
        '''hidden_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_size''': predefined_args['''embed_size'''],
        '''initializer_range''': 0.02,
        '''intermediate_size''': predefined_args['''hidden_size'''],
        '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
        '''max_position_embeddings''': predefined_args['''max_length'''],
        '''model_type''': '''bort''',
        '''num_attention_heads''': predefined_args['''num_heads'''],
        '''num_hidden_layers''': predefined_args['''num_layers'''],
        '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
        '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
        '''vocab_size''': len(__lowerCAmelCase	),
    }

    __lowercase		:			Tuple	    =       BertConfig.from_dict(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       BertForMaskedLM(__lowerCAmelCase	)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(__lowerCAmelCase	) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

    # Check param shapes and map new HF param back
    def check_and_map_params(__lowerCAmelCase	,					__lowerCAmelCase	):
        __lowercase		:			Optional[Any]	    =       hf_param.shape

        __lowercase		:			int	    =       to_torch(params[gluon_param]	)
        __lowercase		:			Union[str, Any]	    =       gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), F'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

        return gluon_param

    __lowercase		:			Union[str, Any]	    =       check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight	,					'''word_embed.0.weight'''	)
    __lowercase		:			Union[str, Any]	    =       check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight	,					'''encoder.position_weight'''	)
    __lowercase		:			List[Any]	    =       check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias	,					'''encoder.layer_norm.beta'''	)
    __lowercase		:			Optional[int]	    =       check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight	,					'''encoder.layer_norm.gamma'''	)

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    __lowercase		:			List[Any]	    =       torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

    for i in range(hf_bort_config.num_hidden_layers	):
        __lowercase		:			BertLayer	    =       hf_bort_model.bert.encoder.layer[i]

        # self attention
        __lowercase		:			BertSelfAttention	    =       layer.attention.self

        __lowercase		:			Union[str, Any]	    =       check_and_map_params(
            self_attn.key.bias.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'	)

        __lowercase		:			List[Any]	    =       check_and_map_params(
            self_attn.key.weight.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'	)
        __lowercase		:			Dict	    =       check_and_map_params(
            self_attn.query.bias.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'	)
        __lowercase		:			Tuple	    =       check_and_map_params(
            self_attn.query.weight.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'	)
        __lowercase		:			Dict	    =       check_and_map_params(
            self_attn.value.bias.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'	)
        __lowercase		:			Any	    =       check_and_map_params(
            self_attn.value.weight.data	,					F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'	)

        # self attention output
        __lowercase		:			BertSelfOutput	    =       layer.attention.output

        __lowercase		:			List[str]	    =       check_and_map_params(
            self_output.dense.bias	,					F'encoder.transformer_cells.{i}.proj.bias'	)
        __lowercase		:			str	    =       check_and_map_params(
            self_output.dense.weight	,					F'encoder.transformer_cells.{i}.proj.weight'	)
        __lowercase		:			str	    =       check_and_map_params(
            self_output.LayerNorm.bias	,					F'encoder.transformer_cells.{i}.layer_norm.beta'	)
        __lowercase		:			int	    =       check_and_map_params(
            self_output.LayerNorm.weight	,					F'encoder.transformer_cells.{i}.layer_norm.gamma'	)

        # intermediate
        __lowercase		:			BertIntermediate	    =       layer.intermediate

        __lowercase		:			int	    =       check_and_map_params(
            intermediate.dense.bias	,					F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'	)
        __lowercase		:			List[Any]	    =       check_and_map_params(
            intermediate.dense.weight	,					F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'	)

        # output
        __lowercase		:			BertOutput	    =       layer.output

        __lowercase		:			Dict	    =       check_and_map_params(
            bert_output.dense.bias	,					F'encoder.transformer_cells.{i}.ffn.ffn_2.bias'	)
        __lowercase		:			Tuple	    =       check_and_map_params(
            bert_output.dense.weight	,					F'encoder.transformer_cells.{i}.ffn.ffn_2.weight'	)
        __lowercase		:			Optional[Any]	    =       check_and_map_params(
            bert_output.LayerNorm.bias	,					F'encoder.transformer_cells.{i}.ffn.layer_norm.beta'	)
        __lowercase		:			Any	    =       check_and_map_params(
            bert_output.LayerNorm.weight	,					F'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'	)

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    __lowercase		:			Any	    =       RobertaTokenizer.from_pretrained('''roberta-base'''	)

    __lowercase		:			Optional[Any]	    =       tokenizer.encode_plus(__lowerCAmelCase	)['''input_ids''']

    # Get gluon output
    __lowercase		:			Optional[Any]	    =       mx.nd.array([input_ids]	)
    __lowercase		:			List[Any]	    =       original_bort(inputs=__lowerCAmelCase	,					token_types=[]	)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       BertModel.from_pretrained(__lowerCAmelCase	)
    hf_bort_model.eval()

    __lowercase		:			Any	    =       tokenizer.encode_plus(__lowerCAmelCase	,					return_tensors='''pt'''	)
    __lowercase		:			Optional[int]	    =       hf_bort_model(**__lowerCAmelCase	)[0]

    __lowercase		:			Any	    =       output_gluon[0].asnumpy()
    __lowercase		:			List[Any]	    =       output_hf[0].detach().numpy()

    __lowercase		:			int	    =       np.max(np.abs(hf_layer - gluon_layer	)	).item()
    __lowercase		:			List[Any]	    =       np.allclose(__lowerCAmelCase	,					__lowerCAmelCase	,					atol=1E-3	)

    if success:
        print('''✔️ Both model do output the same tensors'''	)
    else:
        print('''❌ Both model do **NOT** output the same tensors'''	)
        print('''Absolute difference is:'''	,					__lowerCAmelCase	)


if __name__ == "__main__":
 __lowerCAmelCase					:			Tuple			      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 __lowerCAmelCase					:			Any			      =		parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




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	(	lowerCAmelCase_      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       tempfile.mkdtemp()
          __lowercase		:			Tuple	    =       8

          # DPR tok
          __lowercase		:			Optional[int]	    =       [
              '''[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(_snake_case				,       exist_ok=_snake_case				)
          __lowercase		:			str	    =       os.path.join(_snake_case				,       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[str]	    =       [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __lowercase		:			Optional[Any]	    =       dict(zip(_snake_case				,       range(len(_snake_case				)				)				)				)
          __lowercase		:			int	    =       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __lowercase		:			Any	    =       {'''unk_token''': '''<unk>'''}

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




      def 		snake_case_			(      self		:       List[Any]				):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,       '''dpr_tokenizer'''				)				)




      def 		snake_case_			(      self		:       Dict				):
          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,       '''dpr_tokenizer'''				)				)




      def 		snake_case_			(      self		:       Optional[int]				):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,       '''bart_tokenizer'''				)				)




      def 		snake_case_			(      self		:       str				):
          shutil.rmtree(self.tmpdirname				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			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 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Union[str, Any]	    =       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		:			List[Any]	    =       dataset
              __lowercase		:			str	    =       RagRetriever(
                  _snake_case				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()				,       )
          return retriever




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




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       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		:			Optional[int]	    =       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		:			str	    =       os.path.join(self.tmpdirname				,       '''psgs_w100.tsv.pkl'''				)
          __lowercase		:			Dict	    =       {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
          pickle.dump(_snake_case				,       open(_snake_case				,       '''wb'''				)				)

          __lowercase		:			List[str]	    =       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		:			Optional[int]	    =       RagRetriever(
              _snake_case				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()				)
          return retriever




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			List[str]	    =       1
          __lowercase		:			Tuple	    =       self.get_dummy_canonical_hf_index_retriever()
          __lowercase		:			List[Any]	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase    ,      __lowercase    ,      __lowercase		:			str	    =       retriever.retrieve(_snake_case				,       n_docs=_snake_case				)
          self.assertEqual(retrieved_doc_embeds.shape				,       (2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(_snake_case				)				,       2				)
          self.assertEqual(sorted(doc_dicts[0]				)				,       ['''embeddings''', '''id''', '''text''', '''title''']				)
          self.assertEqual(len(doc_dicts[0]['''id''']				)				,       _snake_case				)
          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 		snake_case_			(      self		:       int				):
          __lowercase		:			int	    =       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		:			Optional[Any]	    =       self.get_dummy_dataset()
                  retriever.save_pretrained(_snake_case				)
                  __lowercase		:			str	    =       RagRetriever.from_pretrained(_snake_case				)
                  self.assertIsInstance(_snake_case				,       _snake_case				)
                  __lowercase		:			List[str]	    =       np.array(
                      [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
                  __lowercase		:			Union[str, Any]	    =       retriever.retrieve(_snake_case				,       n_docs=1				)
                  self.assertTrue(out is not None				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       1
          __lowercase		:			Dict	    =       self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case				)
          __lowercase		:			Optional[Any]	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase    ,      __lowercase    ,      __lowercase		:			Tuple	    =       retriever.retrieve(_snake_case				,       n_docs=_snake_case				)
          self.assertEqual(retrieved_doc_embeds.shape				,       (2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(_snake_case				)				,       2				)
          self.assertEqual(sorted(doc_dicts[0]				)				,       ['''embeddings''', '''id''', '''text''', '''title''']				)
          self.assertEqual(len(doc_dicts[0]['''id''']				)				,       _snake_case				)
          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 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(_snake_case				)
              __lowercase		:			Optional[Any]	    =       RagRetriever.from_pretrained(_snake_case				)
              self.assertIsInstance(_snake_case				,       _snake_case				)
              __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(_snake_case				,       n_docs=1				)
              self.assertTrue(out is not None				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Any	    =       1
          __lowercase		:			List[Any]	    =       self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case				)
          __lowercase		:			Any	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase    ,      __lowercase    ,      __lowercase		:			Union[str, Any]	    =       retriever.retrieve(_snake_case				,       n_docs=_snake_case				)
          self.assertEqual(retrieved_doc_embeds.shape				,       (2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(_snake_case				)				,       2				)
          self.assertEqual(sorted(doc_dicts[0]				)				,       ['''embeddings''', '''id''', '''text''', '''title''']				)
          self.assertEqual(len(doc_dicts[0]['''id''']				)				,       _snake_case				)
          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 		snake_case_			(      self		:       Any				):
          __lowercase		:			str	    =       self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(_snake_case				)
              __lowercase		:			Optional[int]	    =       RagRetriever.from_pretrained(_snake_case				)
              self.assertIsInstance(_snake_case				,       _snake_case				)
              __lowercase		:			Union[str, Any]	    =       np.array(
                  [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
              __lowercase		:			Tuple	    =       retriever.retrieve(_snake_case				,       n_docs=1				)
              self.assertTrue(out is not None				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Optional[int]	    =       1
          __lowercase		:			str	    =       self.get_dummy_legacy_index_retriever()
          __lowercase		:			Tuple	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase    ,      __lowercase    ,      __lowercase		:			Tuple	    =       retriever.retrieve(_snake_case				,       n_docs=_snake_case				)
          self.assertEqual(retrieved_doc_embeds.shape				,       (2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(_snake_case				)				,       2				)
          self.assertEqual(sorted(doc_dicts[0]				)				,       ['''text''', '''title''']				)
          self.assertEqual(len(doc_dicts[0]['''text''']				)				,       _snake_case				)
          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 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Tuple	    =       self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(_snake_case				)
              __lowercase		:			Tuple	    =       RagRetriever.from_pretrained(_snake_case				)
              self.assertIsInstance(_snake_case				,       _snake_case				)
              __lowercase		:			Optional[int]	    =       np.array(
                  [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
              __lowercase		:			Tuple	    =       retriever.retrieve(_snake_case				,       n_docs=1				)
              self.assertTrue(out is not None				)




      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def 		snake_case_			(      self		:       Optional[Any]				):
          import torch

          __lowercase		:			Tuple	    =       1
          __lowercase		:			Any	    =       self.get_dummy_canonical_hf_index_retriever()
          __lowercase		:			str	    =       [[5, 7], [10, 11]]
          __lowercase		:			List[Any]	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase		:			Any	    =       retriever(_snake_case				,       _snake_case				,       prefix=retriever.config.generator.prefix				,       n_docs=_snake_case				)
          __lowercase    ,      __lowercase    ,      __lowercase		:			Tuple	    =       (
              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(_snake_case				,       _snake_case				)
          self.assertIsInstance(_snake_case				,       _snake_case				)
          self.assertIsInstance(_snake_case				,       np.ndarray				)

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




      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.get_dpr_ctx_encoder_tokenizer()
          __lowercase		:			str	    =       1
          __lowercase		:			int	    =       self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case				)
          retriever.set_ctx_encoder_tokenizer(_snake_case				)

          __lowercase		:			Tuple	    =       [[5, 7], [10, 11]]
          __lowercase		:			int	    =       np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,       dtype=np.floataa				)
          __lowercase		:			Any	    =       retriever(_snake_case				,       _snake_case				,       prefix=retriever.config.generator.prefix				,       n_docs=_snake_case				)

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


