




import math

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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE = 1       ,    SCREAMING_SNAKE_CASE = 1       ,    SCREAMING_SNAKE_CASE = 1.0E4       ,    SCREAMING_SNAKE_CASE = False       ,    SCREAMING_SNAKE_CASE = 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(SCREAMING_SNAKE_CASE       ,    dtype=jnp.floataa ) * -log_timescale_increment )
			SCREAMING_SNAKE_CASE_								=						jnp.expand_dims(SCREAMING_SNAKE_CASE       ,    1 ) * jnp.expand_dims(SCREAMING_SNAKE_CASE       ,    0 )

			# scale embeddings
			SCREAMING_SNAKE_CASE_								=						scale * emb

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



class       a_       (     nn.Module     ):
	A    							= 32
	A    							= jnp.floataa





	@nn.compact
	def __call__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



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




class       a_       (     nn.Module     ):
	A    							= 32
	A    							= False
	A    							= 1





	@nn.compact
	def __call__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



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






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__()
				SCREAMING_SNAKE_CASE_								=						nn.ModuleList(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[ControlNetOutput, Tuple]:




				"""simple docstring"""



				for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     self.nets      )      ):
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						controlnet(
							    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )

							# merge samples
							if i == 0:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						down_samples, mid_sample
							else:
										SCREAMING_SNAKE_CASE_								=						[
										    samples_prev + samples_curr
										    for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										]
										mid_block_res_sample += mid_sample

				return down_block_res_samples, mid_block_res_sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    SCREAMING_SNAKE_CASE      ,     is_main_process=SCREAMING_SNAKE_CASE      ,     save_function=SCREAMING_SNAKE_CASE      ,     safe_serialization=SCREAMING_SNAKE_CASE      ,     variant=SCREAMING_SNAKE_CASE      ,     )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						model_path_to_save + f'_{idx}'





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						[]

				# load controlnet and append to list until no controlnet directory exists anymore
				# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
				# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
				SCREAMING_SNAKE_CASE_								=						pretrained_model_path
				while os.path.isdir(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
							controlnets.append(SCREAMING_SNAKE_CASE      )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						pretrained_model_path + f'_{idx}'

				logger.info(f'{len(SCREAMING_SNAKE_CASE      )} controlnets loaded from {pretrained_model_path}.'      )

				if len(SCREAMING_SNAKE_CASE      ) == 0:
							raise ValueError(
							    f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE      )}. Expected at least {pretrained_model_path + "_0"}.'      )

				return cls(SCREAMING_SNAKE_CASE      )
