code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						(DDIMScheduler,)
   __lowerCAmelCase				      =						(("eta", 0.0), ("num_inference_steps", 50))
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					**__A		)      ->  Optional[Any]:
       a					={
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }
  
       config.update(**__A		)
       return config
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					**__A		)      ->  List[str]:
       a					=self.scheduler_classes[0]
       a					=self.get_scheduler_config(**__A		)
       a					=scheduler_class(**__A		)
  
       a	,   a					=10, 0.0
  
       a					=self.dummy_model()
       a					=self.dummy_sample_deter
  
       scheduler.set_timesteps(__A		)
  
       for t in scheduler.timesteps:
           a					=model(__A				,					__A		)
           a					=scheduler.step(__A				,					__A				,					__A				,					__A		).prev_sample
  
       return sample
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       for timesteps in [100, 500, 1000]:
           self.check_over_configs(num_train_timesteps=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=__A		)
  
       a					=self.scheduler_classes[0]
       a					=self.get_scheduler_config(steps_offset=1		)
       a					=scheduler_class(**__A		)
       scheduler.set_timesteps(5		)
       assert torch.equal(scheduler.timesteps				,					torch.LongTensor([801, 601, 401, 201, 1]		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,					[0.002, 0.02, 0.2, 2]		):
           self.check_over_configs(beta_start=__A				,					beta_end=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       self.check_over_configs(thresholding=__A		)
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=__A				,					prediction_type=__A				,					sample_max_value=__A				,					)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       for t in [1, 10, 49]:
           self.check_over_forward(time_step=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       for t, num_inference_steps in zip([1, 10, 50]				,					[10, 50, 500]		):
           self.check_over_forward(time_step=__A				,					num_inference_steps=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       for t, eta in zip([1, 10, 49]				,					[0.0, 0.5, 1.0]		):
           self.check_over_forward(time_step=__A				,					eta=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a					=self.scheduler_classes[0]
       a					=self.get_scheduler_config()
       a					=scheduler_class(**__A		)
  
       assert torch.sum(torch.abs(scheduler._get_variance(0				,					0		) - 0.0		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420				,					400		) - 0.14_771		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980				,					960		) - 0.32_460		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0				,					0		) - 0.0		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487				,					486		) - 0.00_979		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999				,					998		) - 0.02		)		) < 1E-5
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					=self.full_loop()
  
       a					=torch.sum(torch.abs(__A		)		)
       a					=torch.mean(torch.abs(__A		)		)
  
       assert abs(result_sum.item() - 172.0_067		) < 1E-2
       assert abs(result_mean.item() - 0.223_967		) < 1E-3
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       a					=self.full_loop(prediction_type='''v_prediction'''		)
  
       a					=torch.sum(torch.abs(__A		)		)
       a					=torch.mean(torch.abs(__A		)		)
  
       assert abs(result_sum.item() - 52.5_302		) < 1E-2
       assert abs(result_mean.item() - 0.0_684		) < 1E-3
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       # We specify different beta, so that the first alpha is 0.99
       a					=self.full_loop(set_alpha_to_one=__A				,					beta_start=0.01		)
       a					=torch.sum(torch.abs(__A		)		)
       a					=torch.mean(torch.abs(__A		)		)
  
       assert abs(result_sum.item() - 149.8_295		) < 1E-2
       assert abs(result_mean.item() - 0.1_951		) < 1E-3
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       # We specify different beta, so that the first alpha is 0.99
       a					=self.full_loop(set_alpha_to_one=__A				,					beta_start=0.01		)
       a					=torch.sum(torch.abs(__A		)		)
       a					=torch.mean(torch.abs(__A		)		)
  
       assert abs(result_sum.item() - 149.0_784		) < 1E-2
       assert abs(result_mean.item() - 0.1_941		) < 1E-3
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  lowerCamelCase_     :     List[str]    		=						logging.getLogger(__name__)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self		)      ->  Dict:
       a					=False
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
       if not self.initialized:
           a					=RagRetriever(
               __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
           a					=True
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       self.retriever.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a					=False
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
       a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
       a					=self.retriever.index.get_doc_dicts(__A		)
       return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
       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				,					)
  
       a					=retrieval_workers
       a					=question_encoder_tokenizer
       a					=generator_tokenizer
       if len(self.retrieval_workers		) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(__A				,					__A				,					__A				,					__A		)
                   for worker in self.retrieval_workers
               ]		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  Dict:
       if len(self.retrieval_workers		) > 0:
           # Select a random retrieval actor.
           a					=self.retrieval_workers[random.randint(0				,					len(self.retrieval_workers		) - 1		)]
           a	,   a	,   a					=ray.get(
               random_worker.retrieve.remote(__A				,					__A		)		)
       else:
           a	,   a					=self._main_retrieve(__A				,					__A		)
           a					=self.index.get_doc_dicts(__A		)
       return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
       return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
       a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
       a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
       a					=rag_tokenizer.question_encoder
       a					=rag_tokenizer.generator
  
       if indexed_dataset is not None:
           a					='''custom'''
           a					=CustomHFIndex(config.retrieval_vector_size				,					__A		)
       else:
           a					=cls._build_index(__A		)
  
       return cls(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					retrieval_workers=__A				,					index=__A				,					)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''re-loading the new dataset with embeddings'''		)
       # access from the training loop
  
       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]		)
  
       # build the index object again
       a					=self._build_index(self.config		)
  
       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config				,					self.question_encoder_tokenizer				,					self.generator_tokenizer				,					__A		)
               for worker in self.retrieval_workers
           ]		)