





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


snake_case__					:							List[str]													=     r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(_lowerCamelCase   )
class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """rag"""
   lowerCAmelCase__        = True




   def __init__(self	:List[Any]    ,   _UpperCamelCase	:str=None    ,   _UpperCamelCase	:Dict=True    ,   _UpperCamelCase	:str=None    ,   _UpperCamelCase	:Any=None    ,   _UpperCamelCase	:Any=None    ,   _UpperCamelCase	:Optional[Any]=None    ,   _UpperCamelCase	:int=None    ,   _UpperCamelCase	:Union[str, Any]=" / "    ,   _UpperCamelCase	:Any=" // "    ,   _UpperCamelCase	:Optional[Any]=5    ,   _UpperCamelCase	:Any=300    ,   _UpperCamelCase	:List[str]=768    ,   _UpperCamelCase	:Optional[int]=8    ,   _UpperCamelCase	:Union[str, Any]="wiki_dpr"    ,   _UpperCamelCase	:int="train"    ,   _UpperCamelCase	:Any="compressed"    ,   _UpperCamelCase	:int=None    ,   _UpperCamelCase	:Union[str, Any]=None    ,   _UpperCamelCase	:List[Any]=False    ,   _UpperCamelCase	:Any=False    ,   _UpperCamelCase	:List[str]=0.0    ,   _UpperCamelCase	:Any=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:Optional[int]=False    ,   _UpperCamelCase	:int=False    ,   _UpperCamelCase	:List[Any]=True    ,   _UpperCamelCase	:List[Any]=None    ,   **_UpperCamelCase	:List[str]    ,   )->					List[Any]:
         super().__init__(
             bos_token_id=_UpperCamelCase    ,   pad_token_id=_UpperCamelCase    ,   eos_token_id=_UpperCamelCase    ,   decoder_start_token_id=_UpperCamelCase    ,   forced_eos_token_id=_UpperCamelCase    ,   is_encoder_decoder=_UpperCamelCase    ,   prefix=_UpperCamelCase    ,   vocab_size=_UpperCamelCase    ,   **_UpperCamelCase    ,   )
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         __A          =				kwargs.pop('''question_encoder''' )
         __A          =				question_encoder_config.pop('''model_type''' )
         __A          =				kwargs.pop('''generator''' )
         __A          =				decoder_config.pop('''model_type''' )

         from ..auto.configuration_auto import AutoConfig

         __A          =				AutoConfig.for_model(_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				AutoConfig.for_model(_UpperCamelCase    ,   **_UpperCamelCase )

         __A          =				reduce_loss
         __A          =				label_smoothing
         __A          =				exclude_bos_score
         __A          =				do_marginalize

         __A          =				title_sep
         __A          =				doc_sep
         __A          =				n_docs
         __A          =				max_combined_length

         __A          =				dataset
         __A          =				dataset_split
         __A          =				index_name

         __A          =				retrieval_vector_size
         __A          =				retrieval_batch_size
         __A          =				passages_path
         __A          =				index_path
         __A          =				use_dummy_dataset

         __A          =				output_retrieved

         __A          =				do_deduplication

         __A          =				use_cache

         if self.forced_eos_token_id is None:
               __A          =				getattr(self.generator    ,   '''forced_eos_token_id'''    ,   _UpperCamelCase )




   @classmethod
   def _lowerCAmelCase      (cls	:List[str]    ,   _UpperCamelCase	:PretrainedConfig    ,   _UpperCamelCase	:PretrainedConfig    ,   **_UpperCamelCase	:int )->					PretrainedConfig:
         return cls(question_encoder=question_encoder_config.to_dict()    ,   generator=generator_config.to_dict()    ,   **_UpperCamelCase )






   def _lowerCAmelCase      (self	:List[Any] )->					int:
         __A          =				copy.deepcopy(self.__dict__ )
         __A          =				self.question_encoder.to_dict()
         __A          =				self.generator.to_dict()
         __A          =				self.__class__.model_type
         return output












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





