






'''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 					__UpperCAmelCase :

      '''simple docstring'''





      def __init__(					self						):
             UpperCAmelCase__	:   Optional[int]												=	False




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             if not self.initialized:
                    UpperCAmelCase__	:   Tuple												=	RagRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=_UpperCAmelCase  ,		generator_tokenizer=_UpperCAmelCase  ,		index=_UpperCAmelCase  ,		init_retrieval=_UpperCAmelCase  ,		)
                    UpperCAmelCase__	:   Tuple												=	True




      def 							lowerCamelCase     (					self						):
             self.retriever.index.init_index()




      def 							lowerCamelCase     (					self						):
             # delete the old self.retriever object before assigning the new index
             del self.retriever
             UpperCAmelCase__	:   Union[str, Any]												=	False




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             UpperCAmelCase__							,							UpperCAmelCase__	:   str												=	self.retriever._main_retrieve(_UpperCAmelCase  ,		_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	self.retriever.index.get_doc_dicts(_UpperCAmelCase						)
             return doc_ids, retrieved_doc_embeds, doc_dicts




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None						):
             if index is not None and index.is_initialized() and len(_UpperCAmelCase						) > 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__(
                 _UpperCAmelCase  ,		question_encoder_tokenizer=_UpperCAmelCase  ,		generator_tokenizer=_UpperCAmelCase  ,		index=_UpperCAmelCase  ,		init_retrieval=_UpperCAmelCase  ,		)

             UpperCAmelCase__	:   Union[str, Any]												=	retrieval_workers
             UpperCAmelCase__	:   List[str]												=	question_encoder_tokenizer
             UpperCAmelCase__	:   int												=	generator_tokenizer
             if len(self.retrieval_workers						) > 0:
                    ray.get(
                        [
                            worker.create_rag_retriever.remote(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
                            for worker in self.retrieval_workers
                        ]						)




      def 							lowerCamelCase     (					self						):
             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 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             if len(self.retrieval_workers						) > 0:
                    # Select a random retrieval actor.
                    UpperCAmelCase__	:   Dict												=	self.retrieval_workers[random.randint(0  ,		len(self.retrieval_workers						) - 1						)]
                    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[int]												=	ray.get(
                        random_worker.retrieve.remote(_UpperCAmelCase  ,		_UpperCAmelCase						)						)
             else:
                    UpperCAmelCase__							,							UpperCAmelCase__	:   str												=	self._main_retrieve(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   List[Any]												=	self.index.get_doc_dicts(_UpperCAmelCase						)
             return retrieved_doc_embeds, doc_ids, doc_dicts




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		**_UpperCAmelCase						):
             return super(_UpperCAmelCase  ,		cls						).get_tokenizers(_UpperCAmelCase  ,		_UpperCAmelCase  ,		**_UpperCAmelCase						)




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   str												=	kwargs.pop('''config'''  ,		_UpperCAmelCase						) or RagConfig.from_pretrained(_UpperCAmelCase  ,		**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	RagTokenizer.from_pretrained(_UpperCAmelCase  ,		config=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	rag_tokenizer.question_encoder
             UpperCAmelCase__	:   Any												=	rag_tokenizer.generator

             if indexed_dataset is not None:
                    UpperCAmelCase__	:   str												=	'''custom'''
                    UpperCAmelCase__	:   Any												=	CustomHFIndex(config.retrieval_vector_size  ,		_UpperCAmelCase						)
             else:
                    UpperCAmelCase__	:   int												=	cls._build_index(_UpperCAmelCase						)

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




      def 							lowerCamelCase     (					self						):
             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
             UpperCAmelCase__	:   str												=	self._build_index(self.config						)

             ray.get(
                 [
                     worker.create_rag_retriever.remote(
                         self.config  ,		self.question_encoder_tokenizer  ,		self.generator_tokenizer  ,		_UpperCAmelCase						)
                     for worker in self.retrieval_workers
                 ]						)







'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					(DDIMScheduler,)
      SCREAMING_SNAKE_CASE				:    str				       =					(("eta", 0.0), ("num_inference_steps", 5_0))




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   List[str]												=	{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.00_01,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**_UpperCAmelCase						)
             return config




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_scheduler_config(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	scheduler_class(**_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	10, 0.0

             UpperCAmelCase__	:   Optional[Any]												=	self.dummy_model()
             UpperCAmelCase__	:   List[Any]												=	self.dummy_sample_deter

             scheduler.set_timesteps(_UpperCAmelCase						)

             for t in scheduler.timesteps:
                    UpperCAmelCase__	:   Dict												=	model(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   int												=	scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return sample




      def 							lowerCamelCase     (					self						):
             for timesteps in [100, 500, 1000]:
                    self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Any												=	self.get_scheduler_config(steps_offset=1						)
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(5						)
             assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]						)						)




      def 							lowerCamelCase     (					self						):
             for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,		[0.0_02, 0.02, 0.2, 2]						):
                    self.check_over_configs(beta_start=_UpperCAmelCase  ,		beta_end=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             self.check_over_configs(thresholding=_UpperCAmelCase						)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=_UpperCAmelCase  ,		prediction_type=_UpperCAmelCase  ,		sample_max_value=_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             for t in [1, 10, 49]:
                    self.check_over_forward(time_step=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		num_inference_steps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		eta=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   int												=	self.get_scheduler_config()
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400						) - 0.1_47_71						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960						) - 0.3_24_60						)						) < 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						) - 0.0_09_79						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998						) - 0.02						)						) < 1E-5




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.full_loop()

             UpperCAmelCase__	:   Dict												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   str												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67						) < 1E-3




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Any												=	self.full_loop(prediction_type='''v_prediction'''						)

             UpperCAmelCase__	:   Union[str, Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Tuple												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 52.53_02						) < 1E-2
             assert abs(result_mean.item() - 0.06_84						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   List[str]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
             assert abs(result_mean.item() - 0.19_51						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   Optional[Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   int												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
             assert abs(result_mean.item() - 0.19_41						) < 1E-3