


"""simple docstring"""



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase      		=			logging.getLogger(__name__)
class 					__magic_name__		:






							def __init__(   self  :    Dict				):

														'''simple docstring'''






														lowercase		:List[Any]       							=				False






							def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    int  ,     snake_case__  :    Any  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

														'''simple docstring'''






														if not self.initialized:
																					lowercase		:List[str]       							=				RagRetriever(
																					    A_  ,     question_encoder_tokenizer=A_  ,     generator_tokenizer=A_  ,     index=A_  ,     init_retrieval=A_  ,     )
																					lowercase		:Dict       							=				True






							def  __snake_case						(   self  :    Optional[Any]				):

														'''simple docstring'''






														self.retriever.index.init_index()







							def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    int				):

														'''simple docstring'''






														lowercase							,  lowercase		:Tuple       							=				self.retriever._main_retrieve(A_  ,     A_				)
														return doc_ids, retrieved_doc_embeds







class 					__magic_name__		(			_lowercase     ):






							def __init__(   self  :    Union[str, Any]  ,     snake_case__  :    int  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Dict  ,     snake_case__  :    str=None				):

														'''simple docstring'''






														if index is not None and index.is_initialized() and len(A_				) > 0:
																					raise ValueError(
																					    '''When using Ray for distributed fine-tuning, '''
																					    '''you\'ll need to provide the paths instead, '''
																					    '''as the dataset and the index are loaded '''
																					    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''				)
														super().__init__(
														    A_  ,     question_encoder_tokenizer=A_  ,     generator_tokenizer=A_  ,     index=A_  ,     init_retrieval=A_  ,     )
														lowercase		:Any       							=				retrieval_workers
														if len(self.retrieval_workers				) > 0:
																					ray.get(
																					    [
																					        worker.create_rag_retriever.remote(A_  ,     A_  ,     A_  ,     A_				)
																					        for worker in self.retrieval_workers
																					    ]				)






							def  __snake_case						(   self  :    str				):

														'''simple docstring'''






														logger.info('''initializing retrieval'''				)

														if len(self.retrieval_workers				) > 0:
																					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]				)
														else:
																					# Non-distributed training. Load index into this same process.
																					self.index.init_index()






							def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    int				):

														'''simple docstring'''






														if len(self.retrieval_workers				) > 0:
																					# Select a random retrieval actor.
																					lowercase		:Any       							=				self.retrieval_workers[random.randint(0  ,     len(self.retrieval_workers				) - 1				)]
																					lowercase							,  lowercase		:str       							=				ray.get(random_worker.retrieve.remote(A_  ,     A_				)				)
														else:
																					lowercase							,  lowercase		:Any       							=				self._main_retrieve(A_  ,     A_				)
														return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_				)






							@classmethod
							def  __snake_case						(   cls  :    Any  ,     snake_case__  :    List[str]  ,     snake_case__  :    Optional[int]=None  ,     **snake_case__  :    Any				):

														'''simple docstring'''






														return super(A_  ,     cls				).get_tokenizers(A_  ,     A_  ,     **A_				)







							@classmethod
							def  __snake_case						(   cls  :    Any  ,     snake_case__  :    int  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    int=None  ,     **snake_case__  :    List[str]				):

														'''simple docstring'''






														lowercase		:Optional[Any]       							=				kwargs.pop('''config'''  ,     A_				) or RagConfig.from_pretrained(A_  ,     **A_				)
														lowercase		:Tuple       							=				RagTokenizer.from_pretrained(A_  ,     config=A_				)
														lowercase		:Union[str, Any]       							=				rag_tokenizer.question_encoder
														lowercase		:int       							=				rag_tokenizer.generator
														if indexed_dataset is not None:
																					lowercase		:List[str]       							=				'''custom'''
																					lowercase		:Optional[int]       							=				CustomHFIndex(config.retrieval_vector_size  ,     A_				)
														else:
																					lowercase		:Union[str, Any]       							=				cls._build_index(A_				)
														return cls(
														    A_  ,     question_encoder_tokenizer=A_  ,     generator_tokenizer=A_  ,     retrieval_workers=A_  ,     index=A_  ,     )





"""simple docstring"""



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 3,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

              assert image is not None, "Make sure output is not None"







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
              model.to(snake_case__				).eval()

              torch.manual_seed(0				)
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0				)

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
              # fmt: on
              self.assertTrue(torch.allclose(snake_case__  ,     snake_case__  ,     atol=1e-3				)				)

