






import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 						snake_case  (  __lowercase       ,			__lowercase	):





    @register_to_config
    def __init__(							self     : Tuple    ,		*,
        A     : int = 4    ,		A     : int = 7_6_8    ,		A     : int    ,		A     : Dict    ,		):


      '''simple docstring'''




      super().__init__()

      a					:      Any    =       nn.Parameter(torch.zeros(UpperCAmelCase__      )      )

      # parameters for additional clip time embeddings
      a					:      int    =       nn.Linear(UpperCAmelCase__    ,		UpperCAmelCase__      )
      a					:      List[str]    =       nn.Linear(UpperCAmelCase__    ,		UpperCAmelCase__      )

      # parameters for encoder hidden states
      a					:      List[Any]    =       clip_extra_context_tokens
      a					:      List[Any]    =       nn.Linear(
          UpperCAmelCase__    ,		self.clip_extra_context_tokens * cross_attention_dim      )
      a					:      List[Any]    =       nn.Linear(UpperCAmelCase__    ,		UpperCAmelCase__      )
      a					:      Tuple    =       nn.LayerNorm(UpperCAmelCase__      )





    def     lowerCamelCase__				(							self     : int    ,		*, A     : Optional[Any]    ,		A     : int    ,		A     : Any    ,		A     : str      ):


      '''simple docstring'''




      if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        a					:      Dict    =       image_embeddings.shape[0]
        a					:      List[str]    =       self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
        a					:      List[str]    =       classifier_free_guidance_embeddings.expand(
            UpperCAmelCase__    ,		-1      )
        a					:      str    =       torch.cat([classifier_free_guidance_embeddings, image_embeddings]    ,		dim=0      )

      # The image embeddings batch size and the text embeddings batch size are equal
      assert image_embeddings.shape[0] == prompt_embeds.shape[0]

      a					:      int    =       prompt_embeds.shape[0]

      # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
      # adding CLIP embeddings to the existing timestep embedding, ...
      a					:      int    =       self.embedding_proj(UpperCAmelCase__      )
      a					:      Optional[int]    =       self.clip_image_embeddings_project_to_time_embeddings(UpperCAmelCase__      )
      a					:      Union[str, Any]    =       time_projected_image_embeddings + time_projected_prompt_embeds

      # ... and by projecting CLIP embeddings into four
      # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
      a					:      Union[str, Any]    =       self.clip_extra_context_tokens_proj(UpperCAmelCase__      )
      a					:      int    =       clip_extra_context_tokens.reshape(UpperCAmelCase__    ,		-1    ,		self.clip_extra_context_tokens      )
      a					:      str    =       clip_extra_context_tokens.permute(0    ,		2    ,		1      )

      a					:      Dict    =       self.encoder_hidden_states_proj(UpperCAmelCase__      )
      a					:      Dict    =       self.text_encoder_hidden_states_norm(UpperCAmelCase__      )
      a					:      Optional[Any]    =       torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]    ,		dim=1      )

      return text_encoder_hidden_states, additive_clip_time_embeddings












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




