
'''simple docstring'''





import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

a__		: int  							=							logging.get_logger(__name__)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase = True						)->      Optional[Any]:







      """simple docstring"""
      __A													=      {}
      for name in model.attn_processors.keys():
            __A													=      name.endswith('attn2.processor'						) and not (
                name.startswith('transformer_in'						) or 'temp_attentions' in name.split('.'						)
            )
            __A													=      model.config.cross_attention_dim if has_cross_attention else None
            if name.startswith('mid_block'						):
                  __A													=      model.config.block_out_channels[-1]
            elif name.startswith('up_blocks'						):
                  __A													=      int(name[len('up_blocks.'						)]						)
                  __A													=      list(reversed(model.config.block_out_channels						)						)[block_id]
            elif name.startswith('down_blocks'						):
                  __A													=      int(name[len('down_blocks.'						)]						)
                  __A													=      model.config.block_out_channels[block_id]
            elif name.startswith('transformer_in'						):
                  # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                  __A													=      8 * model.config.attention_head_dim

            __A													=      LoRAAttnProcessor(hidden_size=UpperCAmelCase   ,	cross_attention_dim=UpperCAmelCase						)
            __A													=      lora_attn_procs[name].to(model.device						)

            if mock_weights:
                  # add 1 to weights to mock trained weights
                  with torch.no_grad():
                        lora_attn_procs[name].to_q_lora.up.weight += 1
                        lora_attn_procs[name].to_k_lora.up.weight += 1
                        lora_attn_procs[name].to_v_lora.up.weight += 1
                        lora_attn_procs[name].to_out_lora.up.weight += 1

      return lora_attn_procs


@skip_mps
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     List[str]    					=       UNetaDConditionModel
 UpperCAmelCase__     :     Dict    					=       'sample'






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      4
       __A													=      4
       __A													=      4
       __A													=      (32, 32)

       __A													=      floats_tensor((batch_size, num_channels, num_frames) + sizes      ).to(_A      )
       __A													=      torch.tensor([10]      ).to(_A      )
       __A													=      floats_tensor((batch_size, 4, 32)      ).to(_A      )

       return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}






 @property
 def 						lowercase_    (      self	:int      )					->      List[Any]:







       '''simple docstring'''



       return (4, 4, 32, 32)






 @property
 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       return (4, 4, 32, 32)






 def 						lowercase_    (      self	:Tuple      )					->      Any:







       '''simple docstring'''



       __A													=      {
           'block_out_channels': (32, 64),
           'down_block_types': (
               'CrossAttnDownBlock3D',
               'DownBlock3D',
           ),
           'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
           'cross_attention_dim': 32,
           'attention_head_dim': 8,
           'out_channels': 4,
           'in_channels': 4,
           'layers_per_block': 1,
           'sample_size': 32,
       }
       __A													=      self.dummy_input
       return init_dict, inputs_dict






 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()						,					reason='XFormers attention is only available with CUDA and `xformers` installed'						,					)
 def 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()
       __A													=      self.model_class(**_A      )

       model.enable_xformers_memory_efficient_attention()

       assert (
           model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
           == "XFormersAttnProcessor"
       ), "xformers is not enabled"






 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      32

       __A													=      self.model_class(**_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             __A													=      model(**_A      )

             if isinstance(_A						,					_A      ):
                   __A													=      output.sample

       self.assertIsNotNone(_A      )
       __A													=      inputs_dict['sample'].shape
       self.assertEqual(output.shape						,					_A						,					'Input and output shapes do not match'      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()
       __A													=      self.model_class(**_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             # Warmup pass when using mps (see #372)
             if torch_device == "mps" and isinstance(_A						,					_A      ):
                   model(**self.dummy_input      )

             __A													=      model(**_A      )
             if isinstance(_A						,					_A      ):
                   __A													=      first.sample

             __A													=      model(**_A      )
             if isinstance(_A						,					_A      ):
                   __A													=      second.sample

       __A													=      first.cpu().numpy()
       __A													=      second.cpu().numpy()
       __A													=      out_a[~np.isnan(_A      )]
       __A													=      out_a[~np.isnan(_A      )]
       __A													=      np.amax(np.abs(out_a - out_a      )      )
       self.assertLessEqual(_A						,					1E-5      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       __A													=      self.model_class(**_A      )
       model.to(_A      )
       model.eval()

       model.set_attention_slice('auto'      )
       with torch.no_grad():
             __A													=      model(**_A      )
       assert output is not None

       model.set_attention_slice('max'      )
       with torch.no_grad():
             __A													=      model(**_A      )
       assert output is not None

       model.set_attention_slice(2      )
       with torch.no_grad():
             __A													=      model(**_A      )
       assert output is not None






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       __A													=      self.model_class(**_A      )
       model.to(_A      )

       with torch.no_grad():
             __A													=      model(**_A      ).sample

       __A													=      create_lora_layers(_A      )

       # make sure we can set a list of attention processors
       model.set_attn_processor(_A      )
       model.to(_A      )

       # test that attn processors can be set to itself
       model.set_attn_processor(model.attn_processors      )

       with torch.no_grad():
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.0}      ).sample
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample

       assert (samplea - samplea).abs().max() < 3E-3
       assert (samplea - samplea).abs().max() < 3E-3

       # sample 2 and sample 3 should be different
       assert (samplea - samplea).abs().max() > 3E-3






 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )

       with torch.no_grad():
             __A													=      model(**_A      ).sample

       __A													=      create_lora_layers(_A      )
       model.set_attn_processor(_A      )

       with torch.no_grad():
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_attn_procs(_A      )
             self.assertTrue(os.path.isfile(os.path.join(_A						,					'pytorch_lora_weights.bin'      )      )      )
             torch.manual_seed(0      )
             __A													=      self.model_class(**_A      )
             new_model.to(_A      )
             new_model.load_attn_procs(_A      )

       with torch.no_grad():
             __A													=      new_model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample

       assert (sample - new_sample).abs().max() < 1E-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1E-4






 def 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )

       with torch.no_grad():
             __A													=      model(**_A      ).sample

       __A													=      create_lora_layers(_A      )
       model.set_attn_processor(_A      )

       with torch.no_grad():
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_attn_procs(_A						,					safe_serialization=_A      )
             self.assertTrue(os.path.isfile(os.path.join(_A						,					'pytorch_lora_weights.safetensors'      )      )      )
             torch.manual_seed(0      )
             __A													=      self.model_class(**_A      )
             new_model.to(_A      )
             new_model.load_attn_procs(_A      )

       with torch.no_grad():
             __A													=      new_model(**_A						,					cross_attention_kwargs={'scale': 0.5}      ).sample

       assert (sample - new_sample).abs().max() < 3E-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1E-4






 def 						lowercase_    (      self	:Any      )					->      Tuple:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )

       __A													=      create_lora_layers(_A						,					mock_weights=_A      )
       model.set_attn_processor(_A      )
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_attn_procs(_A      )
             self.assertTrue(os.path.isfile(os.path.join(_A						,					'pytorch_lora_weights.bin'      )      )      )
             torch.manual_seed(0      )
             __A													=      self.model_class(**_A      )
             new_model.to(_A      )
             new_model.load_attn_procs(_A						,					weight_name='pytorch_lora_weights.bin'      )






 def 						lowercase_    (      self	:Any      )					->      Union[str, Any]:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )

       __A													=      create_lora_layers(_A						,					mock_weights=_A      )
       model.set_attn_processor(_A      )
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_attn_procs(_A      )
             self.assertTrue(os.path.isfile(os.path.join(_A						,					'pytorch_lora_weights.bin'      )      )      )
             torch.manual_seed(0      )
             __A													=      self.model_class(**_A      )
             new_model.to(_A      )
             with self.assertRaises(_A      ) as e:
                   new_model.load_attn_procs(_A						,					use_safetensors=_A      )
             self.assertIn('Error no file named pytorch_lora_weights.safetensors'						,					str(e.exception      )      )






 def 						lowercase_    (      self	:List[Any]      )					->      str:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      8

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )

       with torch.no_grad():
             __A													=      model(**_A      ).sample

       __A													=      create_lora_layers(_A      )
       model.set_attn_processor(_A      )

       with torch.no_grad():
             __A													=      model(**_A						,					cross_attention_kwargs={'scale': 0.0}      ).sample

       model.set_attn_processor(AttnProcessor()      )

       with torch.no_grad():
             __A													=      model(**_A      ).sample

       assert (sample - new_sample).abs().max() < 1E-4
       assert (sample - old_sample).abs().max() < 3E-3






 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()						,					reason='XFormers attention is only available with CUDA and `xformers` installed'						,					)
 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()

       __A													=      4

       torch.manual_seed(0      )
       __A													=      self.model_class(**_A      )
       model.to(_A      )
       __A													=      create_lora_layers(_A      )
       model.set_attn_processor(_A      )

       # default
       with torch.no_grad():
             __A													=      model(**_A      ).sample

             model.enable_xformers_memory_efficient_attention()
             __A													=      model(**_A      ).sample

             model.disable_xformers_memory_efficient_attention()
             __A													=      model(**_A      ).sample

       assert (sample - on_sample).abs().max() < 1E-4
       assert (sample - off_sample).abs().max() < 1E-4






 def 						lowercase_    (      self	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       __A						,   __A													=      self.prepare_init_args_and_inputs_for_common()
       __A													=      32

       __A													=      self.model_class(**_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             __A													=      model(**_A      )[0]

       model.enable_forward_chunking()
       with torch.no_grad():
             __A													=      model(**_A      )[0]

       self.assertEqual(output.shape						,					output_a.shape						,					'Shape doesn\'t match'      )
       assert np.abs(output.cpu() - output_a.cpu()      ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.








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:					List[Any]     =None

if is_ray_available():
       import ray  # noqa: E402 # isort:skip
       from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
       _UpperCamelCase:					List[str]     =None
       _UpperCamelCase:					Tuple     =None
       _UpperCamelCase:					Any     =None



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




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





@require_distributed_retrieval
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







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

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

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   DPRQuestionEncoderTokenizer:
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,      'dpr_tokenizer'       )       )



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



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



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _lowerCAmelCase						=		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					:			int  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			Tuple=1_2345       )					->   RagPyTorchDistributedRetriever:
         _lowerCAmelCase						=		self.get_dummy_dataset()
         _lowerCAmelCase						=		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:
           _lowerCAmelCase						=		dataset
           _lowerCAmelCase						=		RagPyTorchDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      )
           if init_retrieval:
             retriever.init_retrieval(_lowerCAmelCase       )
         return retriever



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



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



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



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			RagRetriever  ,      _lowerCAmelCase					:			np.array  ,      _lowerCAmelCase					:			int       )					->   None:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		retriever.retrieve(_lowerCAmelCase  ,      n_docs=_lowerCAmelCase       )
         self.assertEqual(retrieved_doc_embeds.shape  ,      (2, n_docs, self.retrieval_vector_size)       )
         self.assertEqual(len(_lowerCAmelCase       )  ,      2       )
         self.assertEqual(sorted(doc_dicts[0]       )  ,      ['embeddings', 'id', 'text', 'title']       )
         self.assertEqual(len(doc_dicts[0]['id']       )  ,      _lowerCAmelCase       )
         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					:			Any       )					->   Optional[int]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		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=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Any:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		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=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[Any]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		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=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      )



       @require_ray
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		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=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         ray.shutdown()



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






       @require_ray
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   List[Any]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		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=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         ray.shutdown()
