
"""simple docstring"""






from typing import Any





def __A						(   a_ :							list		,		a_ :							list		,		a_ :							dict		,		a_ :							dict		,		a_ :							dict		,		)->							Dict:




		'''simple docstring'''






		_validation(
		    __UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		)
		# Creates data structures and fill initial step
		SCREAMING_SNAKE_CASE       :     dict				= {}
		SCREAMING_SNAKE_CASE       :     dict				= {}
		for state in states_space:
				SCREAMING_SNAKE_CASE       :     List[str]				= observations_space[0]
				SCREAMING_SNAKE_CASE       :     Optional[int]				= (
				    initial_probabilities[state] * emission_probabilities[state][observation]
				)
				SCREAMING_SNAKE_CASE       :     List[str]				= None

		# Fills the data structure with the probabilities of
		# different transitions and pointers to previous states
		for o in range(1		,		len(__UpperCamelCase     )     ):
				SCREAMING_SNAKE_CASE       :     List[Any]				= observations_space[o]
				SCREAMING_SNAKE_CASE       :     int				= observations_space[o - 1]
				for state in states_space:
						# Calculates the argmax for probability function
						SCREAMING_SNAKE_CASE       :     int				= """"""
						SCREAMING_SNAKE_CASE       :     List[str]				= -1
						for k_state in states_space:
								SCREAMING_SNAKE_CASE       :     List[str]				= (
								    probabilities[(k_state, prior_observation)]
								    * transition_probabilities[k_state][state]
								    * emission_probabilities[state][observation]
								)
								if probability > max_probability:
										SCREAMING_SNAKE_CASE       :     List[str]				= probability
										SCREAMING_SNAKE_CASE       :     List[Any]				= k_state

            # Update probabilities and pointers dicts
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
						    probabilities[(arg_max, prior_observation)]
						    * transition_probabilities[arg_max][state]
						    * emission_probabilities[state][observation]
						)

						SCREAMING_SNAKE_CASE       :     str				= arg_max

    # The final observation
		SCREAMING_SNAKE_CASE       :     List[str]				= observations_space[len(__UpperCamelCase     ) - 1]

		# argmax for given final observation
		SCREAMING_SNAKE_CASE       :     int				= """"""
		SCREAMING_SNAKE_CASE       :     Optional[int]				= -1
		for k_state in states_space:
				SCREAMING_SNAKE_CASE       :     List[str]				= probabilities[(k_state, final_observation)]
				if probability > max_probability:
						SCREAMING_SNAKE_CASE       :     Any				= probability
						SCREAMING_SNAKE_CASE       :     Tuple				= k_state
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= arg_max

		# Process pointers backwards
		SCREAMING_SNAKE_CASE       :     Tuple				= last_state
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []
		for o in range(len(__UpperCamelCase     ) - 1		,		-1		,		-1     ):
				result.append(__UpperCamelCase     )
				SCREAMING_SNAKE_CASE       :     Dict				= pointers[previous, observations_space[o]]
		result.reverse()

		return result





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		)->							Any:




		'''simple docstring'''






		_validate_not_empty(
		    __UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		)
		_validate_lists(__UpperCamelCase		,		__UpperCamelCase     )
		_validate_dicts(
		    __UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		)->							Any:




		'''simple docstring'''






		if not all(
		    [
		        observations_space,
		        states_space,
		        initial_probabilities,
		        transition_probabilities,
		        emission_probabilities,
		    ]     ):
				raise ValueError('''There\'s an empty parameter'''     )





def __A						(   a_ :							Any		,		a_ :							Any     )->							Optional[int]:




		'''simple docstring'''






		_validate_list(__UpperCamelCase		,		'''observations_space'''     )
		_validate_list(__UpperCamelCase		,		'''states_space'''     )





def __A						(   a_ :							Any		,		a_ :							str     )->							Union[str, Any]:




		'''simple docstring'''






		if not isinstance(_object		,		__UpperCamelCase     ):
				SCREAMING_SNAKE_CASE       :     Any				= F"{var_name} must be a list"
				raise ValueError(__UpperCamelCase     )
		else:
				for x in _object:
						if not isinstance(__UpperCamelCase		,		__UpperCamelCase     ):
								SCREAMING_SNAKE_CASE       :     str				= F"{var_name} must be a list of strings"
								raise ValueError(__UpperCamelCase     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							Any		,		)->							Optional[Any]:




		'''simple docstring'''






		_validate_dict(__UpperCamelCase		,		'''initial_probabilities'''		,		__UpperCamelCase     )
		_validate_nested_dict(__UpperCamelCase		,		'''transition_probabilities'''     )
		_validate_nested_dict(__UpperCamelCase		,		'''emission_probabilities'''     )





def __A						(   a_ :							Any		,		a_ :							str     )->							Union[str, Any]:




		'''simple docstring'''






		_validate_dict(_object		,		__UpperCamelCase		,		__UpperCamelCase     )
		for x in _object.values():
				_validate_dict(__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase		,		__UpperCamelCase     )





def __A						(   a_ :							Any		,		a_ :							str		,		a_ :							type		,		a_ :							bool = False     )->							Optional[int]:




		'''simple docstring'''






		if not isinstance(_object		,		__UpperCamelCase     ):
				SCREAMING_SNAKE_CASE       :     str				= F"{var_name} must be a dict"
				raise ValueError(__UpperCamelCase     )
		if not all(isinstance(__UpperCamelCase		,		__UpperCamelCase     ) for x in _object     ):
				SCREAMING_SNAKE_CASE       :     Any				= F"{var_name} all keys must be strings"
				raise ValueError(__UpperCamelCase     )
		if not all(isinstance(__UpperCamelCase		,		__UpperCamelCase     ) for x in _object.values()     ):
				SCREAMING_SNAKE_CASE       :     int				= """nested dictionary """ if nested else """"""
				SCREAMING_SNAKE_CASE       :     str				= F"{var_name} {nested_text}all values must be {value_type.__name__}"
				raise ValueError(__UpperCamelCase     )


if __name__ == "__main__":
					from doctest import testmod

					testmod()







"""simple docstring"""






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


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

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

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# 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       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

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

			return cls(lowerCamelCase_					)





