

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     (       A_						):



   def        __magic_name__    (							self							:     Union[str, Any]						):
     UpperCAmelCase       :							List[Any]  = tempfile.mkdtemp()
     UpperCAmelCase       :							Tuple  = 8

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

     UpperCAmelCase       :							Optional[int]  = os.path.join(self.tmpdirname,		'''bart_tokenizer'''						)
     os.makedirs(_snake_case,		exist_ok=_snake_case						)
     UpperCAmelCase       :							Optional[Any]  = os.path.join(_snake_case,		BART_VOCAB_FILES_NAMES['''vocab_file''']						)
     UpperCAmelCase       :							Dict  = 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        __magic_name__    (							self							:     Dict						):
     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,		'''dpr_tokenizer'''						)						)



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



   def        __magic_name__    (							self							:     List[Any]						):
     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,		'''bart_tokenizer'''						)						)



   def        __magic_name__    (							self							:     Any						):
     shutil.rmtree(self.tmpdirname						)



   def        __magic_name__    (							self							:     str						):
     UpperCAmelCase       :							Any  = 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        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							int  = self.get_dummy_dataset()
     UpperCAmelCase       :							Dict  = 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       :							str  = dataset
       UpperCAmelCase       :							List[Any]  = RagRetriever(
           _snake_case,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer(),		)
     return retriever



   def        __magic_name__    (							self							:     int,		__A							:     bool						):
     UpperCAmelCase       :							Optional[Any]  = self.get_dummy_dataset()
     UpperCAmelCase       :							Optional[int]  = RagConfig(
         retrieval_vector_size=self.retrieval_vector_size,		question_encoder=DPRConfig().to_dict(),		generator=BartConfig().to_dict(),		index_name='''custom''',		)
     if from_disk:
       UpperCAmelCase       :							List[str]  = os.path.join(self.tmpdirname,		'''dataset'''						)
       UpperCAmelCase       :							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
       UpperCAmelCase       :							List[str]  = RagRetriever(
           _snake_case,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer(),		)
     else:
       UpperCAmelCase       :							str  = 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        __magic_name__    (							self							:     Dict						):
     UpperCAmelCase       :							List[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						)

     UpperCAmelCase       :							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'''						)						)

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

     UpperCAmelCase       :							Dict  = 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       :							List[str]  = RagRetriever(
         _snake_case,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer()						)
     return retriever



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



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



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



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



   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def        __magic_name__    (							self							:     List[str]						):
     import torch

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

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





   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def        __magic_name__    (							self							:     Dict						):
     UpperCAmelCase       :							List[str]  = self.get_dpr_ctx_encoder_tokenizer()
     UpperCAmelCase       :							Dict  = 1
     UpperCAmelCase       :							str  = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
     retriever.set_ctx_encoder_tokenizer(_snake_case						)

     UpperCAmelCase       :							Any  = [[5, 7], [1_0, 1_1]]
     UpperCAmelCase       :							Dict  = np.array(
         [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)],		dtype=np.floataa						)
     UpperCAmelCase       :							Union[str, 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.










"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()
