




import torch
from torch import nn

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




class 		UpperCamelCase__	(lowerCAmelCase__    ,				lowerCAmelCase__     ):

    '''simple docstring'''



    @register_to_config
    def __init__(    self     ,  *,
        UpperCamelCase__ = 4     ,  UpperCamelCase__ = 768     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  )     ->      Union[str, Any]:
           super().__init__()

           lowerCamelCase   :      List[str]			 =					nn.Parameter(torch.zeros(UpperCamelCase__     )     )

           # parameters for additional clip time embeddings
           lowerCamelCase   :      Any			 =					nn.Linear(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					nn.Linear(UpperCamelCase__     ,  UpperCamelCase__     )

           # parameters for encoder hidden states
           lowerCamelCase   :      Dict			 =					clip_extra_context_tokens
           lowerCamelCase   :      Tuple			 =					nn.Linear(
               UpperCamelCase__     ,  self.clip_extra_context_tokens * cross_attention_dim     )
           lowerCamelCase   :      str			 =					nn.Linear(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					nn.LayerNorm(UpperCamelCase__     )







    def     _lowercase     (    self     ,  *, UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Any:
           if do_classifier_free_guidance:
                  # Add the classifier free guidance embeddings to the image embeddings
                  lowerCamelCase   :      Union[str, Any]			 =					image_embeddings.shape[0]
                  lowerCamelCase   :      Optional[Any]			 =					self.learned_classifier_free_guidance_embeddings.unsqueeze(0     )
                  lowerCamelCase   :      Optional[Any]			 =					classifier_free_guidance_embeddings.expand(
                      UpperCamelCase__     ,  -1     )
                  lowerCamelCase   :      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]

           lowerCamelCase   :      Union[str, Any]			 =					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   :      str			 =					self.embedding_proj(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__     )
           lowerCamelCase   :      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"
           lowerCamelCase   :      str			 =					self.clip_extra_context_tokens_proj(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					clip_extra_context_tokens.reshape(UpperCamelCase__     ,  -1     ,  self.clip_extra_context_tokens     )
           lowerCamelCase   :      Optional[int]			 =					clip_extra_context_tokens.permute(0     ,  2     ,  1     )

           lowerCamelCase   :      Optional[Any]			 =					self.encoder_hidden_states_proj(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					self.text_encoder_hidden_states_norm(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,  dim=1     )

           return text_encoder_hidden_states, additive_clip_time_embeddings














"""simple docstring"""





from math import pow, sqrt







def      lowerCamelCase__       (							*_lowerCamelCase       :		float )      ->				bool:
  lowerCamelCase_         =						len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase )
      else ValueError('Input Error: Molar mass values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,				2 )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,				2 ) / molar_mass		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )




