

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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	jnp.ndarray
    __lowerCamelCase						:	jnp.ndarray



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):
    __lowerCamelCase						:	int
    __lowerCamelCase						:	Tuple[int]														=					(16, 32, 96, 256)
    __lowerCamelCase						:	jnp.dtype														=					jnp.floataa





    def 				snake_case_    (				self):
         lowercase__   :       str				=	nn.Conv(
             self.block_out_channels[0]						,       kernel_size=(3, 3)						,       padding=((1, 1), (1, 1))						,       dtype=self.dtype						,       )

         lowercase__   :       int				=	[]
         for i in range(len(self.block_out_channels) - 1):
              lowercase__   :       Optional[int]				=	self.block_out_channels[i]
              lowercase__   :       Optional[Any]				=	self.block_out_channels[i + 1]
              lowercase__   :       Tuple				=	nn.Conv(
                  a						,       kernel_size=(3, 3)						,       padding=((1, 1), (1, 1))						,       dtype=self.dtype						,       )
              blocks.append(a)
              lowercase__   :       List[str]				=	nn.Conv(
                  a						,       kernel_size=(3, 3)						,       strides=(2, 2)						,       padding=((1, 1), (1, 1))						,       dtype=self.dtype						,       )
              blocks.append(a)
         lowercase__   :       Tuple				=	blocks

         lowercase__   :       Tuple				=	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						,       a):
         lowercase__   :       Any				=	self.conv_in(a)
         lowercase__   :       Optional[int]				=	nn.silu(a)

         for block in self.blocks:
              lowercase__   :       Dict				=	block(a)
              lowercase__   :       Optional[Any]				=	nn.silu(a)

         lowercase__   :       List[Any]				=	self.conv_out(a)

         return embedding



@flax_register_to_config
class 				SCREAMING_SNAKE_CASE__ (nn.Module		,	__snake_case		,	__snake_case					):
    __lowerCamelCase						:	int														=					32
    __lowerCamelCase						:	int														=					4
    __lowerCamelCase						:	Tuple[str]														=					(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    __lowerCamelCase						:	Union[bool, Tuple[bool]]														=					False
    __lowerCamelCase						:	Tuple[int]														=					(320, 640, 1280, 1280)
    __lowerCamelCase						:	int														=					2
    __lowerCamelCase						:	Union[int, Tuple[int]]														=					8
    __lowerCamelCase						:	Optional[Union[int, Tuple[int]]]														=					None
    __lowerCamelCase						:	int														=					1280
    __lowerCamelCase						:	float														=					0.0
    __lowerCamelCase						:	bool														=					False
    __lowerCamelCase						:	jnp.dtype														=					jnp.floataa
    __lowerCamelCase						:	bool														=					True
    __lowerCamelCase						:	int														=					0
    __lowerCamelCase						:	str														=					"rgb"
    __lowerCamelCase						:	Tuple[int]														=					(16, 32, 96, 256)





    def 				snake_case_    (				self						,       a):
         # init input tensors
         lowercase__   :       Dict				=	(1, self.in_channels, self.sample_size, self.sample_size)
         lowercase__   :       str				=	jnp.zeros(a						,       dtype=jnp.floataa)
         lowercase__   :       List[str]				=	jnp.ones((1,)						,       dtype=jnp.intaa)
         lowercase__   :       Tuple				=	jnp.zeros((1, 1, self.cross_attention_dim)						,       dtype=jnp.floataa)
         lowercase__   :       Union[str, Any]				=	(1, 3, self.sample_size * 8, self.sample_size * 8)
         lowercase__   :       Dict				=	jnp.zeros(a						,       dtype=jnp.floataa)

         lowercase__   :       Dict				=	jax.random.split(a)
         lowercase__   :       Any				=	{'params': params_rng, 'dropout': dropout_rng}

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





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.block_out_channels
         lowercase__   :       Optional[Any]				=	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.
         lowercase__   :       Optional[Any]				=	self.num_attention_heads or self.attention_head_dim

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

         # time
         lowercase__   :       Dict				=	FlaxTimesteps(
             block_out_channels[0]						,       flip_sin_to_cos=self.flip_sin_to_cos						,       freq_shift=self.config.freq_shift)
         lowercase__   :       Dict				=	FlaxTimestepEmbedding(a						,       dtype=self.dtype)

         lowercase__   :       Optional[int]				=	FlaxControlNetConditioningEmbedding(
             conditioning_embedding_channels=block_out_channels[0]						,       block_out_channels=self.conditioning_embedding_out_channels						,       )

         lowercase__   :       List[str]				=	self.only_cross_attention
         if isinstance(a						,       a):
              lowercase__   :       Tuple				=	(only_cross_attention,) * len(self.down_block_types)

         if isinstance(a						,       a):
              lowercase__   :       Any				=	(num_attention_heads,) * len(self.down_block_types)

         # down
         lowercase__   :       Any				=	[]
         lowercase__   :       Union[str, Any]				=	[]

         lowercase__   :       Union[str, Any]				=	block_out_channels[0]

         lowercase__   :       Optional[int]				=	nn.Conv(
             a						,       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(a)

         for i, down_block_type in enumerate(self.down_block_types):
              lowercase__   :       List[Any]				=	output_channel
              lowercase__   :       Any				=	block_out_channels[i]
              lowercase__   :       List[Any]				=	i == len(a) - 1

              if down_block_type == "CrossAttnDownBlock2D":
                   lowercase__   :       Optional[int]				=	FlaxCrossAttnDownBlockaD(
                       in_channels=a						,       out_channels=a						,       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:
                   lowercase__   :       str				=	FlaxDownBlockaD(
                       in_channels=a						,       out_channels=a						,       dropout=self.dropout						,       num_layers=self.layers_per_block						,       add_downsample=not is_final_block						,       dtype=self.dtype						,       )

              down_blocks.append(a)

              for _ in range(self.layers_per_block):
                   lowercase__   :       Any				=	nn.Conv(
                       a						,       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(a)

              if not is_final_block:
                   lowercase__   :       Dict				=	nn.Conv(
                       a						,       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(a)

         lowercase__   :       str				=	down_blocks
         lowercase__   :       Dict				=	controlnet_down_blocks

         # mid
         lowercase__   :       Any				=	block_out_channels[-1]
         lowercase__   :       Dict				=	FlaxUNetMidBlockaDCrossAttn(
             in_channels=a						,       dropout=self.dropout						,       num_attention_heads=num_attention_heads[-1]						,       use_linear_projection=self.use_linear_projection						,       dtype=self.dtype						,       )

         lowercase__   :       List[Any]				=	nn.Conv(
             a						,       kernel_size=(1, 1)						,       padding='VALID'						,       kernel_init=nn.initializers.zeros_init()						,       bias_init=nn.initializers.zeros_init()						,       dtype=self.dtype						,       )





    def __call__(				self						,       a						,       a						,       a						,       a						,       a = 1.0						,       a = True						,       a = False						,       ):
         lowercase__   :       Optional[Any]				=	self.controlnet_conditioning_channel_order
         if channel_order == "bgr":
              lowercase__   :       Dict				=	jnp.flip(a						,       axis=1)

         # 1. time
         if not isinstance(a						,       jnp.ndarray):
              lowercase__   :       Dict				=	jnp.array([timesteps]						,       dtype=jnp.intaa)
         elif isinstance(a						,       jnp.ndarray) and len(timesteps.shape) == 0:
              lowercase__   :       Optional[Any]				=	timesteps.astype(dtype=jnp.floataa)
              lowercase__   :       Optional[int]				=	jnp.expand_dims(a						,       0)

         lowercase__   :       List[str]				=	self.time_proj(a)
         lowercase__   :       Union[str, Any]				=	self.time_embedding(a)

         # 2. pre-process
         lowercase__   :       List[str]				=	jnp.transpose(a						,       (0, 2, 3, 1))
         lowercase__   :       int				=	self.conv_in(a)

         lowercase__   :       Dict				=	jnp.transpose(a						,       (0, 2, 3, 1))
         lowercase__   :       Union[str, Any]				=	self.controlnet_cond_embedding(a)
         sample += controlnet_cond

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

         # 4. mid
         lowercase__   :       Tuple				=	self.mid_block(a						,       a						,       a						,       deterministic=not train)

         # 5. contronet blocks
         lowercase__   :       List[str]				=	()
         for down_block_res_sample, controlnet_block in zip(a						,       self.controlnet_down_blocks):
              lowercase__   :       Optional[int]				=	controlnet_block(a)
              controlnet_down_block_res_samples += (down_block_res_sample,)

         lowercase__   :       Tuple				=	controlnet_down_block_res_samples

         lowercase__   :       List[str]				=	self.controlnet_mid_block(a)

         # 6. scaling
         lowercase__   :       List[str]				=	[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=a						,       mid_block_res_sample=a)









import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

