




'''simple docstring'''



import copy

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


lowerCamelCase_									=		r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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







@add_start_docstrings(SCREAMING_SNAKE_CASE_  )
class 						lowercase_       (					SCREAMING_SNAKE_CASE_  ):


   """simple docstring"""

   lowerCamelCase_							    =		'''rag'''
   lowerCamelCase_							    =		True



   def __init__(       self   :      Optional[Any]    , __lowerCamelCase   :      int=None    , __lowerCamelCase   :      Dict=True    , __lowerCamelCase   :      str=None    , __lowerCamelCase   :      int=None    , __lowerCamelCase   :      Any=None    , __lowerCamelCase   :      Dict=None    , __lowerCamelCase   :      Union[str, Any]=None    , __lowerCamelCase   :      str=" / "    , __lowerCamelCase   :      Union[str, Any]=" // "    , __lowerCamelCase   :      str=5    , __lowerCamelCase   :      Dict=3_0_0    , __lowerCamelCase   :      List[Any]=7_6_8    , __lowerCamelCase   :      Dict=8    , __lowerCamelCase   :      str="wiki_dpr"    , __lowerCamelCase   :      List[str]="train"    , __lowerCamelCase   :      str="compressed"    , __lowerCamelCase   :      Dict=None    , __lowerCamelCase   :      Optional[Any]=None    , __lowerCamelCase   :      Optional[int]=False    , __lowerCamelCase   :      Tuple=False    , __lowerCamelCase   :      List[str]=0.0    , __lowerCamelCase   :      int=True    , __lowerCamelCase   :      Optional[int]=False    , __lowerCamelCase   :      Optional[Any]=False    , __lowerCamelCase   :      Any=False    , __lowerCamelCase   :      List[Any]=True    , __lowerCamelCase   :      str=None    , **__lowerCamelCase   :      Dict    , ):

      """simple docstring"""
      super().__init__(
          bos_token_id=snake_case__    , pad_token_id=snake_case__    , eos_token_id=snake_case__    , decoder_start_token_id=snake_case__    , forced_eos_token_id=snake_case__    , is_encoder_decoder=snake_case__    , prefix=snake_case__    , vocab_size=snake_case__    , **snake_case__    , )
      assert (
          "question_encoder" in kwargs and "generator" in kwargs
      ), "Config has to be initialized with question_encoder and generator config"
      _SCREAMING_SNAKE_CASE			= kwargs.pop("question_encoder"       )
      _SCREAMING_SNAKE_CASE			= question_encoder_config.pop("model_type"       )
      _SCREAMING_SNAKE_CASE			= kwargs.pop("generator"       )
      _SCREAMING_SNAKE_CASE			= decoder_config.pop("model_type"       )

      from ..auto.configuration_auto import AutoConfig

      _SCREAMING_SNAKE_CASE			= AutoConfig.for_model(snake_case__    , **snake_case__       )
      _SCREAMING_SNAKE_CASE			= AutoConfig.for_model(snake_case__    , **snake_case__       )

      _SCREAMING_SNAKE_CASE			= reduce_loss
      _SCREAMING_SNAKE_CASE			= label_smoothing
      _SCREAMING_SNAKE_CASE			= exclude_bos_score
      _SCREAMING_SNAKE_CASE			= do_marginalize

      _SCREAMING_SNAKE_CASE			= title_sep
      _SCREAMING_SNAKE_CASE			= doc_sep
      _SCREAMING_SNAKE_CASE			= n_docs
      _SCREAMING_SNAKE_CASE			= max_combined_length

      _SCREAMING_SNAKE_CASE			= dataset
      _SCREAMING_SNAKE_CASE			= dataset_split
      _SCREAMING_SNAKE_CASE			= index_name

      _SCREAMING_SNAKE_CASE			= retrieval_vector_size
      _SCREAMING_SNAKE_CASE			= retrieval_batch_size
      _SCREAMING_SNAKE_CASE			= passages_path
      _SCREAMING_SNAKE_CASE			= index_path
      _SCREAMING_SNAKE_CASE			= use_dummy_dataset

      _SCREAMING_SNAKE_CASE			= output_retrieved

      _SCREAMING_SNAKE_CASE			= do_deduplication

      _SCREAMING_SNAKE_CASE			= use_cache

      if self.forced_eos_token_id is None:
         _SCREAMING_SNAKE_CASE			= getattr(self.generator    , "forced_eos_token_id"    , snake_case__       )



   @classmethod
   def 				lowerCAmelCase_     (       cls   :      Tuple    , __lowerCamelCase   :      str    , __lowerCamelCase   :      Union[str, Any]    , **__lowerCamelCase   :      int       ):

      """simple docstring"""
      return cls(question_encoder=question_encoder_config.to_dict()    , generator=generator_config.to_dict()    , **snake_case__       )



   def 				lowerCAmelCase_     (       self   :      Dict       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= copy.deepcopy(self.__dict__       )
      _SCREAMING_SNAKE_CASE			= self.question_encoder.to_dict()
      _SCREAMING_SNAKE_CASE			= self.generator.to_dict()
      _SCREAMING_SNAKE_CASE			= self.__class__.model_type
      return output





'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     42
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @register_to_config
     def __init__(			self   ,						snake_case__ = 6_5536   ,						snake_case__ = None   ,						snake_case__ = 2   ,						snake_case__ = 2   ,						snake_case__ = 0   ,						snake_case__ = "fourier"   ,						snake_case__ = True   ,						snake_case__ = False   ,						snake_case__ = 0.0   ,						snake_case__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,						snake_case__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,						snake_case__ = "UNetMidBlock1D"   ,						snake_case__ = None   ,						snake_case__ = (32, 32, 64)   ,						snake_case__ = None   ,						snake_case__ = 8   ,						snake_case__ = 1   ,						snake_case__ = False   ,						):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase     :				Optional[int]       =							sample_size

            # time
            if time_embedding_type == "fourier":
                   _lowerCAmelCase     :				Tuple       =							GaussianFourierProjection(
                       embedding_size=8   ,						set_W_to_weight=snake_case__   ,						log=snake_case__   ,						flip_sin_to_cos=snake_case__ )
                   _lowerCAmelCase     :				List[Any]       =							2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                   _lowerCAmelCase     :				Optional[Any]       =							Timesteps(
                       block_out_channels[0]   ,						flip_sin_to_cos=snake_case__   ,						downscale_freq_shift=snake_case__ )
                   _lowerCAmelCase     :				Optional[int]       =							block_out_channels[0]

            if use_timestep_embedding:
                   _lowerCAmelCase     :				Any       =							block_out_channels[0] * 4
                   _lowerCAmelCase     :				List[str]       =							TimestepEmbedding(
                       in_channels=snake_case__   ,						time_embed_dim=snake_case__   ,						act_fn=snake_case__   ,						out_dim=block_out_channels[0]   ,						)

            _lowerCAmelCase     :				Tuple       =							nn.ModuleList([] )
            _lowerCAmelCase     :				Tuple       =							None
            _lowerCAmelCase     :				Optional[Any]       =							nn.ModuleList([] )
            _lowerCAmelCase     :				str       =							None

            # down
            _lowerCAmelCase     :				List[str]       =							in_channels
            for i, down_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				str       =							output_channel
                   _lowerCAmelCase     :				Any       =							block_out_channels[i]

                   if i == 0:
                          input_channel += extra_in_channels

                   _lowerCAmelCase     :				Tuple       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				List[str]       =							get_down_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_downsample=not is_final_block or downsample_each_block   ,						)
                   self.down_blocks.append(snake_case__ )

            # mid
            _lowerCAmelCase     :				List[str]       =							get_mid_block(
                snake_case__   ,						in_channels=block_out_channels[-1]   ,						mid_channels=block_out_channels[-1]   ,						out_channels=block_out_channels[-1]   ,						embed_dim=block_out_channels[0]   ,						num_layers=snake_case__   ,						add_downsample=snake_case__   ,						)

            # up
            _lowerCAmelCase     :				Union[str, Any]       =							list(reversed(snake_case__ ) )
            _lowerCAmelCase     :				List[Any]       =							reversed_block_out_channels[0]
            if out_block_type is None:
                   _lowerCAmelCase     :				Tuple       =							out_channels
            else:
                   _lowerCAmelCase     :				Tuple       =							block_out_channels[0]

            for i, up_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				int       =							output_channel
                   _lowerCAmelCase     :				Dict       =							(
                       reversed_block_out_channels[i + 1] if i < len(snake_case__ ) - 1 else final_upsample_channels
                   )

                   _lowerCAmelCase     :				Union[str, Any]       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				Tuple       =							get_up_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_upsample=not is_final_block   ,						)
                   self.up_blocks.append(snake_case__ )
                   _lowerCAmelCase     :				List[str]       =							output_channel

            # out
            _lowerCAmelCase     :				Tuple       =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,						32 )
            _lowerCAmelCase     :				Any       =							get_out_block(
                out_block_type=snake_case__   ,						num_groups_out=snake_case__   ,						embed_dim=block_out_channels[0]   ,						out_channels=snake_case__   ,						act_fn=snake_case__   ,						fc_dim=block_out_channels[-1] // 4   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							timestep
            if not torch.is_tensor(snake_case__ ):
                   _lowerCAmelCase     :				Optional[int]       =							torch.tensor([timesteps]   ,						dtype=torch.long   ,						device=sample.device )
            elif torch.is_tensor(snake_case__ ) and len(timesteps.shape ) == 0:
                   _lowerCAmelCase     :				List[Any]       =							timesteps[None].to(sample.device )

            _lowerCAmelCase     :				int       =							self.time_proj(snake_case__ )
            if self.config.use_timestep_embedding:
                   _lowerCAmelCase     :				Optional[int]       =							self.time_mlp(snake_case__ )
            else:
                   _lowerCAmelCase     :				List[str]       =							timestep_embed[..., None]
                   _lowerCAmelCase     :				Dict       =							timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
                   _lowerCAmelCase     :				Tuple       =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

            # 2. down
            _lowerCAmelCase     :				str       =							()
            for downsample_block in self.down_blocks:
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							downsample_block(hidden_states=snake_case__   ,						temb=snake_case__ )
                   down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                   _lowerCAmelCase     :				Any       =							self.mid_block(snake_case__   ,						snake_case__ )

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks ):
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[-1:]
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[:-1]
                   _lowerCAmelCase     :				Union[str, Any]       =							upsample_block(snake_case__   ,						res_hidden_states_tuple=snake_case__   ,						temb=snake_case__ )

            # 5. post-process
            if self.out_block:
                   _lowerCAmelCase     :				Optional[Any]       =							self.out_block(snake_case__   ,						snake_case__ )

            if not return_dict:
                   return (sample,)

            return UNetaDOutput(sample=snake_case__ )

