

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


lowercase_									=   logging.get_logger(__name__)





class  __lowerCAmelCase     (					__lowerCamelCase							):




  def __init__(				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =nn.ModuleList(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = True							,   )  -> List[Any]:
        '''simple docstring'''






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

              # merge samples
              if i == 0:
                    _lowercase       ,       _lowercase	       =down_samples, mid_sample
              else:
                    _lowercase	       =[
                        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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   )  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =0
        _lowercase	       =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
              _lowercase	       =model_path_to_save + F'''_{idx}'''




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> int:
        '''simple docstring'''






        _lowercase	       =0
        _lowercase	       =[]

        # 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`, ...
        _lowercase	       =pretrained_model_path
        while os.path.isdir(lowerCAmelCase					):
              _lowercase	       =ControlNetModel.from_pretrained(lowerCAmelCase							,   **lowerCAmelCase					)
              controlnets.append(lowerCAmelCase					)

              idx += 1
              _lowercase	       =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					)




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    r'\w+[.]\d+'
	lowercase       =    re.findall(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	for pat in pats:
		lowercase       =    key.replace(__SCREAMING_SNAKE_CASE ,							'_'.join(pat.split('.'							)							)							)
	return key






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    pt_tuple_key[:-1] + ('scale',)
	if (
	    any('norm' in str_ for str_ in pt_tuple_key							)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		lowercase       =    pt_tensor.transpose(2 ,							3 ,							1 ,							0							)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		lowercase       =    pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	lowercase       =    pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	lowercase       =    pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=42							):
	# Step 1: Convert pytorch tensor to numpy
	lowercase       =    {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	lowercase       =    flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE							)							)

	lowercase       =    flatten_dict(__SCREAMING_SNAKE_CASE							)
	lowercase       =    {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		lowercase       =    rename_key(__SCREAMING_SNAKE_CASE							)
		lowercase       =    tuple(renamed_pt_key.split('.'							)							)

		# Correctly rename weight parameters
		lowercase  ,					lowercase       =    rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
				    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''							)

        # also add unexpected weight so that warning is thrown
		lowercase       =    jnp.asarray(__SCREAMING_SNAKE_CASE							)

	return unflatten_dict(__SCREAMING_SNAKE_CASE							)

