
import math

import flax.linen as nn
import jax.numpy as jnp


def    a      ( a		,						a		,						a = 1		,						a = 1		,						a = 1.0E4		,						a = False		,						a = 1.0		,						)     ->jnp.ndarray:




			'''simple docstring'''


			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			SCREAMING_SNAKE_CASE     						= float(embedding_dim // 2	)
			SCREAMING_SNAKE_CASE     						= math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
			SCREAMING_SNAKE_CASE     						= min_timescale * jnp.exp(jnp.arange(a		,						dtype=jnp.floataa	) * -log_timescale_increment	)
			SCREAMING_SNAKE_CASE     						= jnp.expand_dims(a		,						1	) * jnp.expand_dims(a		,						0	)

			# scale embeddings
			SCREAMING_SNAKE_CASE     						= scale * emb

			if flip_sin_to_cos:
						SCREAMING_SNAKE_CASE     						= jnp.concatenate([jnp.cos(a	), jnp.sin(a	)]		,						axis=1	)
			else:
						SCREAMING_SNAKE_CASE     						= jnp.concatenate([jnp.sin(a	), jnp.cos(a	)]		,						axis=1	)
			SCREAMING_SNAKE_CASE     						= jnp.reshape(a		,						[jnp.shape(a	)[0], embedding_dim]	)
			return signal





class     lowerCamelCase      (       nn.Module					):
	UpperCamelCase_  :					int    		=	3_2
	UpperCamelCase_  :					jnp.dtype    		=	jnp.floataa







	@nn.compact
	def __call__(     self      :Optional[Any]   ,     lowercase      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= nn.Dense(self.time_embed_dim   ,     dtype=self.dtype   ,     name='''linear_1'''			)(lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.silu(lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Dense(self.time_embed_dim   ,     dtype=self.dtype   ,     name='''linear_2'''			)(lowercase			)
				return temb





class     lowerCamelCase      (       nn.Module					):
	UpperCamelCase_  :					int    		=	3_2
	UpperCamelCase_  :					bool    		=	False
	UpperCamelCase_  :					float    		=	1







	@nn.compact
	def __call__(     self      :int   ,     lowercase      :str			)		->      Union[str, Any]:







				"""simple docstring"""






				return get_sinusoidal_embeddings(
				    lowercase   ,     embedding_dim=self.dim   ,     flip_sin_to_cos=self.flip_sin_to_cos   ,     freq_shift=self.freq_shift			)

import torch
from torch import nn

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





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):







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







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE     						= nn.Parameter(torch.zeros(lowercase			)			)

				# parameters for additional clip time embeddings
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)

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







	def  snake_case__     (     self      :Union[str, Any]   ,     *, lowercase      :Optional[int]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






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

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

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

				return text_encoder_hidden_states, additive_clip_time_embeddings