'''simple docstring'''




__UpperCAmelCase												=8.3144598






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   float:





    if temperature < 0:
        raise Exception('''Temperature cannot be less than 0 K'''    )
    if molar_mass <= 0:
        raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol'''    )
    else:
        return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # example
  __UpperCAmelCase												=3_0_0
  __UpperCAmelCase												=2_8
  __UpperCAmelCase												=rms_speed_of_molecule(temperature, molar_mass)
  print(f'Vrms of Nitrogen gas at 300 K is {vrms} m/s')







'''simple docstring'''




import torch
from torch import nn

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




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):






     @register_to_config
     def __init__(			self		:		Any  ,    *,
         a		:		int = 4  ,    a		:		int = 7_68  ,    a		:		int  ,    a		:		Optional[int]  ,    ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase          =  nn.Parameter(torch.zeros(a   )   )

         # parameters for additional clip time embeddings
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )

         # parameters for encoder hidden states
         __lowerCamelCase          =  clip_extra_context_tokens
         __lowerCamelCase          =  nn.Linear(
             a  ,    self.clip_extra_context_tokens * cross_attention_dim   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.LayerNorm(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    *, a		:		Union[str, Any]  ,    a		:		Any  ,    a		:		str  ,    a		:		Any   ):





         """simple docstring"""



         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __lowerCamelCase          =  image_embeddings.shape[0]
             __lowerCamelCase          =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0   )
             __lowerCamelCase          =  classifier_free_guidance_embeddings.expand(
                 a  ,    -1   )
             __lowerCamelCase          =  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]

         __lowerCamelCase          =  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, ...
         __lowerCamelCase          =  self.embedding_proj(a   )
         __lowerCamelCase          =  self.clip_image_embeddings_project_to_time_embeddings(a   )
         __lowerCamelCase          =  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"
         __lowerCamelCase          =  self.clip_extra_context_tokens_proj(a   )
         __lowerCamelCase          =  clip_extra_context_tokens.reshape(a  ,    -1  ,    self.clip_extra_context_tokens   )
         __lowerCamelCase          =  clip_extra_context_tokens.permute(0  ,    2  ,    1   )

         __lowerCamelCase          =  self.encoder_hidden_states_proj(a   )
         __lowerCamelCase          =  self.text_encoder_hidden_states_norm(a   )
         __lowerCamelCase          =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  ,    dim=1   )

         return text_encoder_hidden_states, additive_clip_time_embeddings
