






"""simple docstring"""


import math

from numpy import inf
from scipy.integrate import quad




def     snake_case						(A_   :float   ):







  '''simple docstring'''

  if num <= 0:
    raise ValueError('math domain error'   )

  return quad(A_			,    0			,    A_			,    args=(A_)   )[0]




def     snake_case						(A_   :float			,    A_   :float   ):







  '''simple docstring'''

  return math.pow(A_			,    z - 1   ) * math.exp(-x   )


if __name__ == "__main__":
   from doctest import testmod

   testmod()












"""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




