





'''simple docstring'''


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     (LMSDiscreteScheduler,)
		__lowerCAmelCase								=     10







		def 							A  (self    :   Union[str, Any]   ,   **_lowerCAmelCase    :   List[str]   ):
			A					=   {
			    """num_train_timesteps""": 1100,
			    """beta_start""": 0.0_001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			}

			config.update(**_lowerCAmelCase   )
			return config







		def 							A  (self    :   Optional[int]   ):
			for timesteps in [10, 50, 100, 1000]:
				self.check_over_configs(num_train_timesteps=_lowerCAmelCase   )







		def 							A  (self    :   List[Any]   ):
			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,   [0.0_002, 0.002, 0.02]   ):
				self.check_over_configs(beta_start=_lowerCAmelCase   ,   beta_end=_lowerCAmelCase   )







		def 							A  (self    :   str   ):
			for schedule in ["linear", "scaled_linear"]:
				self.check_over_configs(beta_schedule=_lowerCAmelCase   )







		def 							A  (self    :   Dict   ):
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=_lowerCAmelCase   )







		def 							A  (self    :   Any   ):
			for t in [0, 500, 800]:
				self.check_over_forward(time_step=_lowerCAmelCase   )







		def 							A  (self    :   Dict   ):
			A					=   self.scheduler_classes[0]
			A					=   self.get_scheduler_config()
			A					=   scheduler_class(**_lowerCAmelCase   )

			scheduler.set_timesteps(self.num_inference_steps   )

			A					=   self.dummy_model()
			A					=   self.dummy_sample_deter * scheduler.init_noise_sigma

			for i, t in enumerate(scheduler.timesteps   ):
				A					=   scheduler.scale_model_input(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   scheduler.step(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   output.prev_sample

			A					=   torch.sum(torch.abs(_lowerCAmelCase   )   )
			A					=   torch.mean(torch.abs(_lowerCAmelCase   )   )

			assert abs(result_sum.item() - 1_006.388   ) < 1e-2
			assert abs(result_mean.item() - 1.31   ) < 1e-3







		def 							A  (self    :   Tuple   ):
			A					=   self.scheduler_classes[0]
			A					=   self.get_scheduler_config(prediction_type="""v_prediction"""   )
			A					=   scheduler_class(**_lowerCAmelCase   )

			scheduler.set_timesteps(self.num_inference_steps   )

			A					=   self.dummy_model()
			A					=   self.dummy_sample_deter * scheduler.init_noise_sigma

			for i, t in enumerate(scheduler.timesteps   ):
				A					=   scheduler.scale_model_input(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   scheduler.step(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   output.prev_sample

			A					=   torch.sum(torch.abs(_lowerCAmelCase   )   )
			A					=   torch.mean(torch.abs(_lowerCAmelCase   )   )

			assert abs(result_sum.item() - 0.0_017   ) < 1e-2
			assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3







		def 							A  (self    :   List[str]   ):
			A					=   self.scheduler_classes[0]
			A					=   self.get_scheduler_config()
			A					=   scheduler_class(**_lowerCAmelCase   )

			scheduler.set_timesteps(self.num_inference_steps   ,   device=_lowerCAmelCase   )

			A					=   self.dummy_model()
			A					=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			A					=   sample.to(_lowerCAmelCase   )

			for i, t in enumerate(scheduler.timesteps   ):
				A					=   scheduler.scale_model_input(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   scheduler.step(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   output.prev_sample

			A					=   torch.sum(torch.abs(_lowerCAmelCase   )   )
			A					=   torch.mean(torch.abs(_lowerCAmelCase   )   )

			assert abs(result_sum.item() - 1_006.388   ) < 1e-2
			assert abs(result_mean.item() - 1.31   ) < 1e-3







		def 							A  (self    :   Optional[Any]   ):
			A					=   self.scheduler_classes[0]
			A					=   self.get_scheduler_config()
			A					=   scheduler_class(**_lowerCAmelCase   ,   use_karras_sigmas=_lowerCAmelCase   )

			scheduler.set_timesteps(self.num_inference_steps   ,   device=_lowerCAmelCase   )

			A					=   self.dummy_model()
			A					=   self.dummy_sample_deter.to(_lowerCAmelCase   ) * scheduler.init_noise_sigma
			A					=   sample.to(_lowerCAmelCase   )

			for t in scheduler.timesteps:
				A					=   scheduler.scale_model_input(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   )

				A					=   scheduler.step(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   output.prev_sample

			A					=   torch.sum(torch.abs(_lowerCAmelCase   )   )
			A					=   torch.mean(torch.abs(_lowerCAmelCase   )   )

			assert abs(result_sum.item() - 3_812.9_927   ) < 2e-2
			assert abs(result_mean.item() - 4.9_648   ) < 1e-3








'''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 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







		def 							A  (self    :   List[str]   ):
			self.retriever.index.init_index()







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase   ) > 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__(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )

			A					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   generator_tokenizer
			if len(self.retrieval_workers   ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				        for worker in self.retrieval_workers
				    ]   )







		def 							A  (self    :   Optional[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 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			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(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

			return cls(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   retrieval_workers=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   )







		def 							A  (self    :   str   ):
			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   ,   _lowerCAmelCase   )
			        for worker in self.retrieval_workers
			    ]   )

