

"""simple docstring"""


import torch
from torch import nn

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





class    snake_case     (    __snake_case,							__snake_case  ):

				@register_to_config
				def __init__(     self							:    List[str]					,  *,
				    UpperCamelCase__							:    int = 4					,  UpperCamelCase__							:    int = 7_6_8					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[Any]					,  )->       List[Any]:



						'''simple docstring'''


						super().__init__()

						__lowerCAmelCase:	List[Any]   				=      nn.Parameter(torch.zeros(UpperCamelCase__))

						# parameters for additional clip time embeddings
						__lowerCAmelCase:	Any   				=      nn.Linear(UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      nn.Linear(UpperCamelCase__					,  UpperCamelCase__)

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




				def 							lowercase_					(     self							:    List[Any]					,  *, UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str])->       Optional[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:	Dict   				=      self.encoder_hidden_states_proj(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.text_encoder_hidden_states_norm(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,  dim=1)

						return text_encoder_hidden_states, additive_clip_time_embeddings





"""simple docstring"""


from __future__ import annotations

from math import pi






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     dict[str, float]:

		if (inductance, frequency, reactance).count(0			) != 1:
				raise ValueError("One and only one argument must be 0"			)
		if inductance < 0:
				raise ValueError("Inductance cannot be negative"			)
		if frequency < 0:
				raise ValueError("Frequency cannot be negative"			)
		if reactance < 0:
				raise ValueError("Inductive reactance cannot be negative"			)
		if inductance == 0:
				return {"inductance": reactance / (2 * pi * frequency)}
		elif frequency == 0:
				return {"frequency": reactance / (2 * pi * inductance)}
		elif reactance == 0:
				return {"reactance": 2 * pi * frequency * inductance}
		else:
				raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


