


'''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:
      __UpperCAmelCase     :int												=  None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      __UpperCAmelCase     :int												=  None
      __UpperCAmelCase     :int												=  None
      __UpperCAmelCase     :Optional[Any]												=  None




def       _a      (		_lowercase   :      Any    ):





     '''simple docstring'''






     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
          __UpperCAmelCase      :					Tuple    				=     unittest.skip('''test requires Datasets, Faiss, psutil'''    )(_lowercase    )
     return test_case



@require_distributed_retrieval
class       a							(						_a			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						List[Any]     )		->				Optional[Any]:
          __UpperCAmelCase      :					Any    				=     tempfile.mkdtemp()
          __UpperCAmelCase      :					List[str]    				=     8

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

          __UpperCAmelCase      :					str    				=     os.path.join(self.tmpdirname				,					'''bart_tokenizer'''     )
          os.makedirs(snake_case				,					exist_ok=snake_case     )
          __UpperCAmelCase      :					Any    				=     os.path.join(snake_case				,					BART_VOCAB_FILES_NAMES['''vocab_file''']     )
          __UpperCAmelCase      :					Union[str, Any]    				=     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 					lowerCamelCase__					(							self	:						int     )		->				DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,					'''dpr_tokenizer'''     )     )

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

     def 					lowerCamelCase__					(							self	:						Union[str, Any]     )		->				Any:
          shutil.rmtree(self.tmpdirname     )

     def 					lowerCamelCase__					(							self	:						str     )		->				Optional[int]:
          __UpperCAmelCase      :					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 					lowerCamelCase__					(							self	:						str				,					snake_case	:						bool				,					snake_case	:						Dict=1_2345     )		->				RagPyTorchDistributedRetriever:
          __UpperCAmelCase      :					int    				=     self.get_dummy_dataset()
          __UpperCAmelCase      :					Any    				=     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      :					Optional[int]    				=     dataset
               __UpperCAmelCase      :					Optional[int]    				=     RagPyTorchDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					)
               if init_retrieval:
                    retriever.init_retrieval(snake_case     )
          return retriever

     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						bool     )		->				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=snake_case     )
          __UpperCAmelCase      :					Tuple    				=     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,					question_encoder=DPRConfig().to_dict()				,					generator=BartConfig().to_dict()				,					)
          __UpperCAmelCase      :					str    				=     ray.remote(snake_case     )
          __UpperCAmelCase      :					Tuple    				=     [remote_cls.remote() for _ in range(1     )]
          with patch('''transformers.models.rag.retrieval_rag.load_dataset'''     ) as mock_load_dataset:
               __UpperCAmelCase      :					List[Any]    				=     self.get_dummy_dataset()
               __UpperCAmelCase      :					Any    				=     RagRayDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					retrieval_workers=snake_case				,					)
               if init_retrieval:
                    retriever.init_retrieval()
          return retriever

     def 					lowerCamelCase__					(							self	:						int				,					snake_case	:						bool				,					snake_case	:						bool				,					snake_case	:						Dict=1_2345     )		->				Dict:
          __UpperCAmelCase      :					Optional[int]    				=     self.get_dummy_dataset()
          __UpperCAmelCase      :					Any    				=     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,					question_encoder=DPRConfig().to_dict()				,					generator=BartConfig().to_dict()				,					index_name='''custom'''				,					)
          if from_disk:
               __UpperCAmelCase      :					Optional[Any]    				=     os.path.join(self.tmpdirname				,					'''dataset'''     )
               __UpperCAmelCase      :					List[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      :					int    				=     RagPyTorchDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					)
          else:
               __UpperCAmelCase      :					Dict    				=     RagPyTorchDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					index=CustomHFIndex(config.retrieval_vector_size				,					snake_case     )				,					)
          if init_retrieval:
               retriever.init_retrieval(snake_case     )
          return retriever

     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						bool				,					snake_case	:						bool     )		->				Union[str, Any]:
          # 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=snake_case     )
          __UpperCAmelCase      :					str    				=     self.get_dummy_dataset()
          __UpperCAmelCase      :					str    				=     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size				,					question_encoder=DPRConfig().to_dict()				,					generator=BartConfig().to_dict()				,					index_name='''custom'''				,					)
          __UpperCAmelCase      :					Optional[Any]    				=     ray.remote(snake_case     )
          __UpperCAmelCase      :					str    				=     [remote_cls.remote() for _ in range(1     )]
          if from_disk:
               __UpperCAmelCase      :					Tuple    				=     os.path.join(self.tmpdirname				,					'''dataset'''     )
               __UpperCAmelCase      :					Optional[int]    				=     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      :					Any    				=     RagRayDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					retrieval_workers=snake_case				,					index=CustomHFIndex.load_from_disk(
                       vector_size=config.retrieval_vector_size				,					dataset_path=config.passages_path				,					index_path=config.index_path				,					)				,					)
          else:
               __UpperCAmelCase      :					List[str]    				=     RagRayDistributedRetriever(
                   snake_case				,					question_encoder_tokenizer=self.get_dpr_tokenizer()				,					generator_tokenizer=self.get_bart_tokenizer()				,					retrieval_workers=snake_case				,					index=CustomHFIndex(config.retrieval_vector_size				,					snake_case     )				,					)
          if init_retrieval:
               retriever.init_retrieval()
          return retriever

     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						RagRetriever				,					snake_case	:						np.array				,					snake_case	:						int     )		->				None:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					Optional[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 					lowerCamelCase__					(							self	:						List[Any]     )		->				Union[str, Any]:
          __UpperCAmelCase      :					Dict    				=     1
          __UpperCAmelCase      :					int    				=     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=snake_case     )				,					snake_case				,					snake_case     )

     def 					lowerCamelCase__					(							self	:						List[str]     )		->				int:
          __UpperCAmelCase      :					Optional[Any]    				=     1
          __UpperCAmelCase      :					List[Any]    				=     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=snake_case				,					from_disk=snake_case     )				,					snake_case				,					snake_case				,					)

     def 					lowerCamelCase__					(							self	:						Dict     )		->				List[Any]:
          __UpperCAmelCase      :					Optional[Any]    				=     1
          __UpperCAmelCase      :					List[Any]    				=     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=snake_case				,					from_disk=snake_case     )				,					snake_case				,					snake_case				,					)

     @require_ray
     def 					lowerCamelCase__					(							self	:						Optional[int]     )		->				List[Any]:
          __UpperCAmelCase      :					Any    				=     1
          __UpperCAmelCase      :					Dict    				=     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=snake_case     )				,					snake_case				,					snake_case     )
          ray.shutdown()

     @require_ray
     def 					lowerCamelCase__					(							self	:						Optional[int]     )		->				Any:
          __UpperCAmelCase      :					Dict    				=     1
          __UpperCAmelCase      :					List[str]    				=     np.array(
              [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]				,					dtype=np.floataa     )
          with self.assertRaises(snake_case     ):
               self.distributed_retriever_check(
                   self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case				,					from_disk=snake_case     )				,					snake_case				,					snake_case				,					)
          ray.shutdown()






     @require_ray
     def 					lowerCamelCase__					(							self	:						Tuple     )		->				Any:
          __UpperCAmelCase      :					Any    				=     1
          __UpperCAmelCase      :					Union[str, Any]    				=     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=snake_case				,					from_disk=snake_case     )				,					snake_case				,					snake_case     )
          ray.shutdown()



'''simple docstring'''



def       _a      (		_lowercase   :      str    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Union[str, Any]    				=     5381
     for x in s:
          __UpperCAmelCase      :					Union[str, Any]    				=     ((hash_value << 5) + hash_value) + ord(_lowercase    )
     return hash_value & 0XFFFFFFFF