






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					int									=		{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Optional[Any]									=		[
       'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TimeSeriesTransformerForPrediction',
       'TimeSeriesTransformerModel',
       'TimeSeriesTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_time_series_transformer import (
       TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TimeSeriesTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_time_series_transformer import (
          TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TimeSeriesTransformerForPrediction,
          TimeSeriesTransformerModel,
          TimeSeriesTransformerPreTrainedModel,
      )

else:
   import sys

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












"""simple docstring"""


import torch
from torch import nn

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






class 		snake_case						(						UpperCAmelCase     ,				UpperCAmelCase ):





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


      '''simple docstring'''




      super().__init__()

      a  :    Optional[Any]       =     nn.Parameter(torch.zeros(A    )    )

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

      # parameters for encoder hidden states
      a  :    Optional[Any]       =     clip_extra_context_tokens
      a  :    Union[str, Any]       =     nn.Linear(
          A       ,	self.clip_extra_context_tokens * cross_attention_dim    )
      a  :    Optional[Any]       =     nn.Linear(A       ,	A    )
      a  :    Any       =     nn.LayerNorm(A    )





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


      '''simple docstring'''




      if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        a  :    Dict       =     image_embeddings.shape[0]
        a  :    Tuple       =     self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
        a  :    List[str]       =     classifier_free_guidance_embeddings.expand(
            A       ,	-1    )
        a  :    Union[str, Any]       =     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  :    Dict       =     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  :    Optional[int]       =     self.embedding_proj(A    )
      a  :    Optional[int]       =     self.clip_image_embeddings_project_to_time_embeddings(A    )
      a  :    Tuple       =     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(A    )
      a  :    Dict       =     clip_extra_context_tokens.reshape(A       ,	-1       ,	self.clip_extra_context_tokens    )
      a  :    Union[str, Any]       =     clip_extra_context_tokens.permute(0       ,	2       ,	1    )

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

      return text_encoder_hidden_states, additive_clip_time_embeddings




