


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCAmelCase__   	=							datasets.utils.logging.get_logger(__name__)







@dataclass
class       lowerCamelCase__   (       datasets.BuilderConfig):
     SCREAMING_SNAKE_CASE__      			=			10000
     SCREAMING_SNAKE_CASE__      			=			None
     SCREAMING_SNAKE_CASE__      			=			None







class       lowerCamelCase__   (       datasets.ArrowBasedBuilder):
     SCREAMING_SNAKE_CASE__      			=			ParquetConfig





     def      __A					(self       )     ->   Union[str, Any]:
      return datasets.DatasetInfo(features=self.config.features       )





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      if not self.config.data_files:
       raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"       )
      _lowercase							      =dl_manager.download_and_extract(self.config.data_files       )
      if isinstance(UpperCAmelCase		,     (str, list, tuple)       ):
       _lowercase							      =data_files
       if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       _lowercase							      =[dl_manager.iter_files(UpperCAmelCase       ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={'''files''': files}       )]
      _lowercase							      =[]
      for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       _lowercase							      =[dl_manager.iter_files(UpperCAmelCase       ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
        for file in itertools.chain.from_iterable(UpperCAmelCase       ):
         with open(UpperCAmelCase		,     '''rb'''       ) as f:
          _lowercase							      =datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase       )       )
         break
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase		,     gen_kwargs={'''files''': files}       )       )
      return splits





     def      __A					(self		,     UpperCAmelCase       )     ->   pa.Table:
      if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       _lowercase							      =table_cast(UpperCAmelCase		,     self.info.features.arrow_schema       )
      return pa_table





     def      __A					(self		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =self.info.features.arrow_schema if self.info.features is not None else None
      if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema       ) != sorted(self.config.columns       ):
        raise ValueError(
            f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"       )
      for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase       )       ):
       with open(UpperCAmelCase		,     '''rb'''       ) as f:
        _lowercase							      =pq.ParquetFile(UpperCAmelCase       )
        try:
         for batch_idx, record_batch in enumerate(
             parquet_file.iter_batches(batch_size=self.config.batch_size		,     columns=self.config.columns       )       ):
          _lowercase							      =pa.Table.from_batches([record_batch]       )
          # Uncomment for debugging (will print the Arrow table size and elements)
          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
          yield f"{file_idx}_{batch_idx}", self._cast_table(UpperCAmelCase       )
        except ValueError as e:
         logger.error(f"Failed to read file '{file}' with error {type(UpperCAmelCase       )}: {e}"       )
         raise








'''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     (    __lowerCamelCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any	):
        __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(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  os.path.join(__A							,	DPR_VOCAB_FILES_NAMES['vocab_file']	)
        with open(self.vocab_file							,	'w'							,	encoding='utf-8'	) as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

        # BART tok
        __UpperCamelCase       =  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        __UpperCamelCase       =  dict(zip(__A							,	range(len(__A	)	)	)	)
        __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(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  os.path.join(__A							,	BART_VOCAB_FILES_NAMES['vocab_file']	)
        __UpperCamelCase       =  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	):
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'bart_tokenizer'	)	)






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






 def        _lowerCamelCase					(       self       :							Dict	):
        __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        _lowerCamelCase					(       self       :							Tuple	):
        __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(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        return retriever






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							bool	):
        __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(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        else:
               __UpperCamelCase       =  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       :							int	):
        __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(__A							,	open(__A							,	'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(
            __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()	)
        return retriever






 def        _lowerCamelCase					(       self       :							List[str]	):
        __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(__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       :							Optional[Any]	):
        __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(__A	)
                      __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
                      self.assertIsInstance(__A							,	__A	)
                      __UpperCamelCase       =  np.array(
                          [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
                      __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
                      self.assertTrue(out is not None	)






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






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






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






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

        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)
        __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(__A							,	__A	)
        self.assertIsInstance(__A							,	__A	)
        self.assertIsInstance(__A							,	np.ndarray	)

        __UpperCamelCase       =  retriever(
            __A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A							,	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(__A							,	torch.Tensor	)
        self.assertIsInstance(__A							,	torch.Tensor	)
        self.assertIsInstance(__A							,	torch.Tensor	)






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.get_dpr_ctx_encoder_tokenizer()
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        retriever.set_ctx_encoder_tokenizer(__A	)

        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  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.
