

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class      UpperCamelCase_						(					lowerCamelCase__   ):
					lowerCAmelCase_							=		42
					lowerCAmelCase_							=		42





class      UpperCamelCase_						(					nn.Module   ):
					lowerCAmelCase_							=		42
					lowerCAmelCase_							=		(16, 32, 96, 256)
					lowerCAmelCase_							=		jnp.floataa




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			nn.Conv(
										    self.block_out_channels[0]			,  kernel_size=(3, 3)			,  padding=((1, 1), (1, 1))			,  dtype=self.dtype			,  )

										_snake_case													=			[]
										for i in range(len(self.block_out_channels					) - 1					):
															_snake_case													=			self.block_out_channels[i]
															_snake_case													=			self.block_out_channels[i + 1]
															_snake_case													=			nn.Conv(
															    lowerCAmelCase_			,  kernel_size=(3, 3)			,  padding=((1, 1), (1, 1))			,  dtype=self.dtype			,  )
															blocks.append(lowerCAmelCase_					)
															_snake_case													=			nn.Conv(
															    lowerCAmelCase_			,  kernel_size=(3, 3)			,  strides=(2, 2)			,  padding=((1, 1), (1, 1))			,  dtype=self.dtype			,  )
															blocks.append(lowerCAmelCase_					)
										_snake_case													=			blocks

										_snake_case													=			nn.Conv(
										    self.conditioning_embedding_channels			,  kernel_size=(3, 3)			,  padding=((1, 1), (1, 1))			,  kernel_init=nn.initializers.zeros_init()			,  bias_init=nn.initializers.zeros_init()			,  dtype=self.dtype			,  )






					def __call__(						self			,  lowerCAmelCase_					)						->							Dict:
										_snake_case													=			self.conv_in(lowerCAmelCase_					)
										_snake_case													=			nn.silu(lowerCAmelCase_					)

										for block in self.blocks:
															_snake_case													=			block(lowerCAmelCase_					)
															_snake_case													=			nn.silu(lowerCAmelCase_					)

										_snake_case													=			self.conv_out(lowerCAmelCase_					)

										return embedding





@flax_register_to_config
class      UpperCamelCase_						(					nn.Module							,					lowerCamelCase__							,					lowerCamelCase__   ):
					lowerCAmelCase_							=		32
					lowerCAmelCase_							=		4
					lowerCAmelCase_							=		(
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		(320, 640, 1280, 1280)
					lowerCAmelCase_							=		2
					lowerCAmelCase_							=		8
					lowerCAmelCase_							=		None
					lowerCAmelCase_							=		1280
					lowerCAmelCase_							=		0.0
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		jnp.floataa
					lowerCAmelCase_							=		True
					lowerCAmelCase_							=		0
					lowerCAmelCase_							=		"rgb"
					lowerCAmelCase_							=		(16, 32, 96, 256)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										_snake_case													=			(1, self.in_channels, self.sample_size, self.sample_size)
										_snake_case													=			jnp.zeros(lowerCAmelCase_			,  dtype=jnp.floataa					)
										_snake_case													=			jnp.ones((1,)			,  dtype=jnp.intaa					)
										_snake_case													=			jnp.zeros((1, 1, self.cross_attention_dim)			,  dtype=jnp.floataa					)
										_snake_case													=			(1, 3, self.sample_size * 8, self.sample_size * 8)
										_snake_case													=			jnp.zeros(lowerCAmelCase_			,  dtype=jnp.floataa					)

										_snake_case							,       _snake_case													=			jax.random.split(lowerCAmelCase_					)
										_snake_case													=			{'params': params_rng, 'dropout': dropout_rng}

										return self.init(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)["params"]




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			self.block_out_channels
										_snake_case													=			block_out_channels[0] * 4

										# If `num_attention_heads` is not defined (which is the case for most models)
										# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
										# The reason for this behavior is to correct for incorrectly named variables that were introduced
										# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
										# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
										# which is why we correct for the naming here.
										_snake_case													=			self.num_attention_heads or self.attention_head_dim

										# input
										_snake_case													=			nn.Conv(
										    block_out_channels[0]			,  kernel_size=(3, 3)			,  strides=(1, 1)			,  padding=((1, 1), (1, 1))			,  dtype=self.dtype			,  )

										# time
										_snake_case													=			FlaxTimesteps(
										    block_out_channels[0]			,  flip_sin_to_cos=self.flip_sin_to_cos			,  freq_shift=self.config.freq_shift					)
										_snake_case													=			FlaxTimestepEmbedding(lowerCAmelCase_			,  dtype=self.dtype					)

										_snake_case													=			FlaxControlNetConditioningEmbedding(
										    conditioning_embedding_channels=block_out_channels[0]			,  block_out_channels=self.conditioning_embedding_out_channels			,  )

										_snake_case													=			self.only_cross_attention
										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			(only_cross_attention,) * len(self.down_block_types					)

										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			(num_attention_heads,) * len(self.down_block_types					)

										# down
										_snake_case													=			[]
										_snake_case													=			[]

										_snake_case													=			block_out_channels[0]

										_snake_case													=			nn.Conv(
										    lowerCAmelCase_			,  kernel_size=(1, 1)			,  padding='VALID'			,  kernel_init=nn.initializers.zeros_init()			,  bias_init=nn.initializers.zeros_init()			,  dtype=self.dtype			,  )
										controlnet_down_blocks.append(lowerCAmelCase_					)

										for i, down_block_type in enumerate(self.down_block_types					):
															_snake_case													=			output_channel
															_snake_case													=			block_out_channels[i]
															_snake_case													=			i == len(lowerCAmelCase_					) - 1

															if down_block_type == "CrossAttnDownBlock2D":
																				_snake_case													=			FlaxCrossAttnDownBlockaD(
																				    in_channels=lowerCAmelCase_			,  out_channels=lowerCAmelCase_			,  dropout=self.dropout			,  num_layers=self.layers_per_block			,  num_attention_heads=num_attention_heads[i]			,  add_downsample=not is_final_block			,  use_linear_projection=self.use_linear_projection			,  only_cross_attention=only_cross_attention[i]			,  dtype=self.dtype			,  )
															else:
																				_snake_case													=			FlaxDownBlockaD(
																				    in_channels=lowerCAmelCase_			,  out_channels=lowerCAmelCase_			,  dropout=self.dropout			,  num_layers=self.layers_per_block			,  add_downsample=not is_final_block			,  dtype=self.dtype			,  )

															down_blocks.append(lowerCAmelCase_					)

															for _ in range(self.layers_per_block					):
																				_snake_case													=			nn.Conv(
																				    lowerCAmelCase_			,  kernel_size=(1, 1)			,  padding='VALID'			,  kernel_init=nn.initializers.zeros_init()			,  bias_init=nn.initializers.zeros_init()			,  dtype=self.dtype			,  )
																				controlnet_down_blocks.append(lowerCAmelCase_					)

															if not is_final_block:
																				_snake_case													=			nn.Conv(
																				    lowerCAmelCase_			,  kernel_size=(1, 1)			,  padding='VALID'			,  kernel_init=nn.initializers.zeros_init()			,  bias_init=nn.initializers.zeros_init()			,  dtype=self.dtype			,  )
																				controlnet_down_blocks.append(lowerCAmelCase_					)

										_snake_case													=			down_blocks
										_snake_case													=			controlnet_down_blocks

										# mid
										_snake_case													=			block_out_channels[-1]
										_snake_case													=			FlaxUNetMidBlockaDCrossAttn(
										    in_channels=lowerCAmelCase_			,  dropout=self.dropout			,  num_attention_heads=num_attention_heads[-1]			,  use_linear_projection=self.use_linear_projection			,  dtype=self.dtype			,  )

										_snake_case													=			nn.Conv(
										    lowerCAmelCase_			,  kernel_size=(1, 1)			,  padding='VALID'			,  kernel_init=nn.initializers.zeros_init()			,  bias_init=nn.initializers.zeros_init()			,  dtype=self.dtype			,  )






					def __call__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 1.0			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = False			,  )						->							str:
										_snake_case													=			self.controlnet_conditioning_channel_order
										if channel_order == "bgr":
															_snake_case													=			jnp.flip(lowerCAmelCase_			,  axis=1					)

										# 1. time
										if not isinstance(lowerCAmelCase_			,  jnp.ndarray					):
															_snake_case													=			jnp.array([timesteps]			,  dtype=jnp.intaa					)
										elif isinstance(lowerCAmelCase_			,  jnp.ndarray					) and len(timesteps.shape					) == 0:
															_snake_case													=			timesteps.astype(dtype=jnp.floataa					)
															_snake_case													=			jnp.expand_dims(lowerCAmelCase_			,  0					)

										_snake_case													=			self.time_proj(lowerCAmelCase_					)
										_snake_case													=			self.time_embedding(lowerCAmelCase_					)

										# 2. pre-process
										_snake_case													=			jnp.transpose(lowerCAmelCase_			,  (0, 2, 3, 1)					)
										_snake_case													=			self.conv_in(lowerCAmelCase_					)

										_snake_case													=			jnp.transpose(lowerCAmelCase_			,  (0, 2, 3, 1)					)
										_snake_case													=			self.controlnet_cond_embedding(lowerCAmelCase_					)
										sample += controlnet_cond

										# 3. down
										_snake_case													=			(sample,)
										for down_block in self.down_blocks:
															if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
																				_snake_case							,       _snake_case													=			down_block(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  deterministic=not train					)
															else:
																				_snake_case							,       _snake_case													=			down_block(lowerCAmelCase_			,  lowerCAmelCase_			,  deterministic=not train					)
															down_block_res_samples += res_samples

										# 4. mid
										_snake_case													=			self.mid_block(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  deterministic=not train					)

										# 5. contronet blocks
										_snake_case													=			()
										for down_block_res_sample, controlnet_block in zip(lowerCAmelCase_			,  self.controlnet_down_blocks					):
															_snake_case													=			controlnet_block(lowerCAmelCase_					)
															controlnet_down_block_res_samples += (down_block_res_sample,)

										_snake_case													=			controlnet_down_block_res_samples

										_snake_case													=			self.controlnet_mid_block(lowerCAmelCase_					)

										# 6. scaling
										_snake_case													=			[sample * conditioning_scale for sample in down_block_res_samples]
										mid_block_res_sample *= conditioning_scale

										if not return_dict:
															return (down_block_res_samples, mid_block_res_sample)

										return FlaxControlNetOutput(
										    down_block_res_samples=lowerCAmelCase_			,  mid_block_res_sample=lowerCAmelCase_					)










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

					# extract state_dict for VQVAE
					_snake_case													=			{}
					_snake_case													=			'first_stage_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

    # extract state_dict for UNetLDM
					_snake_case													=			{}
					_snake_case													=			'model.diffusion_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			DDIMScheduler(
					    timesteps=config.model.params.timesteps    ,      beta_schedule='scaled_linear'    ,      beta_start=config.model.params.linear_start    ,      beta_end=config.model.params.linear_end    ,      clip_sample=UpperCamelCase__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					parser.add_argument("""--checkpoint_path""", type=str, required=True)
					parser.add_argument("""--config_path""", type=str, required=True)
					parser.add_argument("""--output_path""", type=str, required=True)
					UpperCAmelCase_ 							=       parser.parse_args()

					convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)



