


'''simple docstring'''





__lowerCamelCase												=						8.3_14_45_98


def        a__     (					UpperCamelCase_						:      float,   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
    __lowerCamelCase												=						300
    __lowerCamelCase												=						28
    __lowerCamelCase												=						rms_speed_of_molecule(temperature, molar_mass)
    print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



