


"""simple docstring"""







import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
       from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
       lowerCamelCase_  :  List[str]     		=    None

if is_ray_available():
       import ray  # noqa: E402 # isort:skip
       from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
       lowerCamelCase_  :  int     		=    None
       lowerCamelCase_  :  Tuple     		=    None
       lowerCamelCase_  :  Union[str, Any]     		=    None
def _A		(		lowercase						):







    """simple docstring"""
    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        a											=unittest.skip('''test requires Datasets, Faiss, psutil'''						)(lowercase						)
    return test_case






@require_distributed_retrieval
class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=tempfile.mkdtemp()
     a											=8

     # DPR tok
     a											=[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     a											=os.path.join(self.tmpdirname					,       '''dpr_tokenizer'''		)
     os.makedirs(__A					,       exist_ok=__A		)
     a											=os.path.join(__A					,       DPR_VOCAB_FILES_NAMES['''vocab_file''']		)
     with open(self.vocab_file					,       '''w'''					,       encoding='''utf-8'''		) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)

     # BART tok
     a											=[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     a											=dict(zip(__A					,       range(len(__A		)		)		)		)
     a											=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     a											={'''unk_token''': '''<unk>'''}

     a											=os.path.join(self.tmpdirname					,       '''bart_tokenizer'''		)
     os.makedirs(__A					,       exist_ok=__A		)
     a											=os.path.join(__A					,       BART_VOCAB_FILES_NAMES['''vocab_file''']		)
     a											=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  SCREAMING_SNAKE_CASE				(						self		)   ->	DPRQuestionEncoderTokenizer:
     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,       '''dpr_tokenizer'''		)		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	BartTokenizer:
     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,       '''bart_tokenizer'''		)		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     shutil.rmtree(self.tmpdirname		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=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  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A=1_2345		)   ->	RagPyTorchDistributedRetriever:
     a											=self.get_dummy_dataset()
     a											=RagConfig(
         retrieval_vector_size=self.retrieval_vector_size					,       question_encoder=DPRConfig().to_dict()					,       generator=BartConfig().to_dict()					,       )
     with patch('''transformers.models.rag.retrieval_rag.load_dataset'''		) as mock_load_dataset:
         a											=dataset
         a											=RagPyTorchDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       )
         if init_retrieval:
             retriever.init_retrieval(__A		)
     return retriever


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	RagRayDistributedRetriever:
     # Have to run in local mode because sys.path modifications at top of
     # file are not propogated to remote workers.
     # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
     ray.init(local_mode=__A		)
     a											=RagConfig(
         retrieval_vector_size=self.retrieval_vector_size					,       question_encoder=DPRConfig().to_dict()					,       generator=BartConfig().to_dict()					,       )
     a											=ray.remote(__A		)
     a											=[remote_cls.remote() for _ in range(1		)]
     with patch('''transformers.models.rag.retrieval_rag.load_dataset'''		) as mock_load_dataset:
         a											=self.get_dummy_dataset()
         a											=RagRayDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       retrieval_workers=__A					,       )
         if init_retrieval:
             retriever.init_retrieval()
     return retriever


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A=1_2345		)   ->	List[Any]:
     a											=self.get_dummy_dataset()
     a											=RagConfig(
         retrieval_vector_size=self.retrieval_vector_size					,       question_encoder=DPRConfig().to_dict()					,       generator=BartConfig().to_dict()					,       index_name='''custom'''					,       )
     if from_disk:
         a											=os.path.join(self.tmpdirname					,       '''dataset'''		)
         a											=os.path.join(self.tmpdirname					,       '''index.faiss'''		)
         dataset.get_index('''embeddings'''		).save(os.path.join(self.tmpdirname					,       '''index.faiss'''		)		)
         dataset.drop_index('''embeddings'''		)
         dataset.save_to_disk(os.path.join(self.tmpdirname					,       '''dataset'''		)		)
         del dataset
         a											=RagPyTorchDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       )
     else:
         a											=RagPyTorchDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       index=CustomHFIndex(config.retrieval_vector_size					,       __A		)					,       )
     if init_retrieval:
         retriever.init_retrieval(__A		)
     return retriever


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	Dict:
     # Have to run in local mode because sys.path modifications at top of
     # file are not propogated to remote workers.
     # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
     ray.init(local_mode=__A		)
     a											=self.get_dummy_dataset()
     a											=RagConfig(
         retrieval_vector_size=self.retrieval_vector_size					,       question_encoder=DPRConfig().to_dict()					,       generator=BartConfig().to_dict()					,       index_name='''custom'''					,       )
     a											=ray.remote(__A		)
     a											=[remote_cls.remote() for _ in range(1		)]
     if from_disk:
         a											=os.path.join(self.tmpdirname					,       '''dataset'''		)
         a											=os.path.join(self.tmpdirname					,       '''index.faiss'''		)
         dataset.get_index('''embeddings'''		).save(os.path.join(self.tmpdirname					,       '''index.faiss'''		)		)
         dataset.drop_index('''embeddings'''		)
         dataset.save_to_disk(os.path.join(self.tmpdirname					,       '''dataset'''		)		)
         del dataset
         a											=RagRayDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       retrieval_workers=__A					,       index=CustomHFIndex.load_from_disk(
                 vector_size=config.retrieval_vector_size					,       dataset_path=config.passages_path					,       index_path=config.index_path					,       )					,       )
     else:
         a											=RagRayDistributedRetriever(
             __A					,       question_encoder_tokenizer=self.get_dpr_tokenizer()					,       generator_tokenizer=self.get_bart_tokenizer()					,       retrieval_workers=__A					,       index=CustomHFIndex(config.retrieval_vector_size					,       __A		)					,       )
     if init_retrieval:
         retriever.init_retrieval()
     return retriever


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A		)   ->	None:
     a						, a						, a											=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  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)

     self.distributed_retriever_check(
         self.get_dummy_pytorch_distributed_retriever(init_retrieval=__A		)					,       __A					,       __A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__A					,       from_disk=__A		)					,       __A					,       __A					,       )


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__A					,       from_disk=__A		)					,       __A					,       __A					,       )


 @require_ray
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)

     self.distributed_retriever_check(
         self.get_dummy_ray_distributed_retriever(init_retrieval=__A		)					,       __A					,       __A		)
     ray.shutdown()


 @require_ray
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)
     with self.assertRaises(__A		):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__A					,       from_disk=__A		)					,       __A					,       __A					,       )
     ray.shutdown()





 @require_ray
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=1
     a											=np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,       dtype=np.floataa		)

     self.distributed_retriever_check(
         self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__A					,       from_disk=__A		)					,       __A					,       __A		)
     ray.shutdown()



"""simple docstring"""







import pytest

from datasets.utils.version import Version
@pytest.mark.parametrize(
    '''other, expected_equality'''			,       [
        (Version('''1.0.0'''						), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''						), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]			,       )
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=Version('''1.0.0'''						)
    assert (version == other) is expected_equality
    assert (version != other) is not expected_equality
    assert (hash(lowercase						) == hash(lowercase						)) is expected_equality