
"""simple docstring"""



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCamelCase__          =	False





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self     ,	UpperCamelCase_=32      ):
          set_seed(0      )
          UpperCAmelCase__	:      int     							=					UNetaDModel(sample_size=UpperCamelCase_     ,	in_channels=3     ,	out_channels=3      )
          UpperCAmelCase__	:      Optional[int]     							=					torch.optim.SGD(model.parameters()     ,	lr=0.0001      )
          return model, optimizer



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
          UpperCAmelCase__	:      Tuple     							=					DDPMScheduler(
              num_train_timesteps=1_000     ,	beta_start=0.0001     ,	beta_end=0.02     ,	beta_schedule='linear'     ,	clip_sample=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(
              num_train_timesteps=1_000     ,	beta_start=0.0001     ,	beta_end=0.02     ,	beta_schedule='linear'     ,	clip_sample=UpperCamelCase_     ,	)

          assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

          # shared batches for DDPM and DDIM
          set_seed(0      )
          UpperCAmelCase__	:      Dict     							=					[torch.randn((4, 3, 32, 32)      ).clip(-1     ,	1      ).to(UpperCamelCase_      ) for _ in range(4      )]
          UpperCAmelCase__	:      Tuple     							=					[torch.randn((4, 3, 32, 32)      ).to(UpperCamelCase_      ) for _ in range(4      )]
          UpperCAmelCase__	:      Any     							=					[torch.randint(0     ,	1_000     ,	(4,)      ).long().to(UpperCamelCase_      ) for _ in range(4      )]

          # train with a DDPM scheduler
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.get_model_optimizer(resolution=32      )
          model.train().to(UpperCamelCase_      )
          for i in range(4      ):
              optimizer.zero_grad()
              UpperCAmelCase__	:      Tuple     							=					ddpm_scheduler.add_noise(clean_images[i]     ,	noise[i]     ,	timesteps[i]      )
              UpperCAmelCase__	:      Dict     							=					model(UpperCamelCase_     ,	timesteps[i]      ).sample
              UpperCAmelCase__	:      Optional[int]     							=					torch.nn.functional.mse_loss(UpperCamelCase_     ,	noise[i]      )
              loss.backward()
              optimizer.step()
          del model, optimizer

          # recreate the model and optimizer, and retry with DDIM
          UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					self.get_model_optimizer(resolution=32      )
          model.train().to(UpperCamelCase_      )
          for i in range(4      ):
              optimizer.zero_grad()
              UpperCAmelCase__	:      Union[str, Any]     							=					ddim_scheduler.add_noise(clean_images[i]     ,	noise[i]     ,	timesteps[i]      )
              UpperCAmelCase__	:      List[str]     							=					model(UpperCamelCase_     ,	timesteps[i]      ).sample
              UpperCAmelCase__	:      Optional[int]     							=					torch.nn.functional.mse_loss(UpperCamelCase_     ,	noise[i]      )
              loss.backward()
              optimizer.step()
          del model, optimizer

          self.assertTrue(torch.allclose(UpperCamelCase_     ,	UpperCamelCase_     ,	atol=1E-5      )      )
          self.assertTrue(torch.allclose(UpperCamelCase_     ,	UpperCamelCase_     ,	atol=1E-5      )      )



"""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 				a			:



      def __init__(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					False



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if not self.initialized:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Union[str, Any]     							=					True



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



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.retriever._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return doc_ids, retrieved_doc_embeds





class 				a			(					lowercase      ):



      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
          if len(self.retrieval_workers      ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                      for worker in self.retrieval_workers
                  ]      )



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



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          return super(UpperCamelCase_     ,	cls      ).get_tokenizers(UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )



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

