




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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)




@flax.struct.dataclass
class    _A					(		_lowercase  ):
		__a            =							42




@flax_register_to_config
class    _A					(		nn.Module							,  _lowercase							,  _lowercase  ):
		__a            =							32
		__a            =							4
		__a            =							4
		__a            =							(
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		__a            =							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
		__a            =							False
		__a            =							(3_20, 6_40, 12_80, 12_80)
		__a            =							2
		__a            =							8
		__a            =							None
		__a            =							12_80
		__a            =							0.0
		__a            =							False
		__a            =							jnp.floataa
		__a            =							True
		__a            =							0
		__a            =							False






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE      ):
								# init input tensors
								_UpperCAmelCase	     =      (1, self.in_channels, self.sample_size, self.sample_size)
								_UpperCAmelCase	     =      jnp.zeros(__A    ,		dtype=jnp.floataa      )
								_UpperCAmelCase	     =      jnp.ones((1,)    ,		dtype=jnp.intaa      )
								_UpperCAmelCase	     =      jnp.zeros((1, 1, self.cross_attention_dim)    ,		dtype=jnp.floataa      )

								_UpperCAmelCase	     =      jax.random.split(__A      )
								_UpperCAmelCase	     =      {"""params""": params_rng, """dropout""": dropout_rng}

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






		def 					UpperCAmelCase			(     self      ):
								_UpperCAmelCase	     =      self.block_out_channels
								_UpperCAmelCase	     =      block_out_channels[0] * 4

								if self.num_attention_heads is not None:
														raise ValueError(
														    """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""      )

								# 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.
								_UpperCAmelCase	     =      self.num_attention_heads or self.attention_head_dim

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

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

								_UpperCAmelCase	     =      self.only_cross_attention
								if isinstance(__A    ,		__A      ):
														_UpperCAmelCase	     =      (only_cross_attention,) * len(self.down_block_types      )

								if isinstance(__A    ,		__A      ):
														_UpperCAmelCase	     =      (num_attention_heads,) * len(self.down_block_types      )

								# down
								_UpperCAmelCase	     =      []
								_UpperCAmelCase	     =      block_out_channels[0]
								for i, down_block_type in enumerate(self.down_block_types      ):
														_UpperCAmelCase	     =      output_channel
														_UpperCAmelCase	     =      block_out_channels[i]
														_UpperCAmelCase	     =      i == len(__A      ) - 1

														if down_block_type == "CrossAttnDownBlock2D":
																				_UpperCAmelCase	     =      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]    ,		use_memory_efficient_attention=self.use_memory_efficient_attention    ,		dtype=self.dtype    ,		)
														else:
																				_UpperCAmelCase	     =      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      )
								_UpperCAmelCase	     =      down_blocks

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

								# up
								_UpperCAmelCase	     =      []
								_UpperCAmelCase	     =      list(reversed(__A      )      )
								_UpperCAmelCase	     =      list(reversed(__A      )      )
								_UpperCAmelCase	     =      list(reversed(__A      )      )
								_UpperCAmelCase	     =      reversed_block_out_channels[0]
								for i, up_block_type in enumerate(self.up_block_types      ):
														_UpperCAmelCase	     =      output_channel
														_UpperCAmelCase	     =      reversed_block_out_channels[i]
														_UpperCAmelCase	     =      reversed_block_out_channels[min(i + 1    ,		len(__A      ) - 1      )]

														_UpperCAmelCase	     =      i == len(__A      ) - 1

														if up_block_type == "CrossAttnUpBlock2D":
																				_UpperCAmelCase	     =      FlaxCrossAttnUpBlockaD(
																				    in_channels=__A    ,		out_channels=__A    ,		prev_output_channel=__A    ,		num_layers=self.layers_per_block + 1    ,		num_attention_heads=reversed_num_attention_heads[i]    ,		add_upsample=not is_final_block    ,		dropout=self.dropout    ,		use_linear_projection=self.use_linear_projection    ,		only_cross_attention=only_cross_attention[i]    ,		use_memory_efficient_attention=self.use_memory_efficient_attention    ,		dtype=self.dtype    ,		)
														else:
																				_UpperCAmelCase	     =      FlaxUpBlockaD(
																				    in_channels=__A    ,		out_channels=__A    ,		prev_output_channel=__A    ,		num_layers=self.layers_per_block + 1    ,		add_upsample=not is_final_block    ,		dropout=self.dropout    ,		dtype=self.dtype    ,		)

														up_blocks.append(__A      )
														_UpperCAmelCase	     =      output_channel
								_UpperCAmelCase	     =      up_blocks

								# out
								_UpperCAmelCase	     =      nn.GroupNorm(num_groups=32    ,		epsilon=1e-5      )
								_UpperCAmelCase	     =      nn.Conv(
								    self.out_channels    ,		kernel_size=(3, 3)    ,		strides=(1, 1)    ,		padding=((1, 1), (1, 1))    ,		dtype=self.dtype    ,		)






		def __call__(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE=None    ,		_SCREAMING_SNAKE_CASE=None    ,		_SCREAMING_SNAKE_CASE = True    ,		_SCREAMING_SNAKE_CASE = False    ,		):
								# 1. time
								if not isinstance(__A    ,		jnp.ndarray      ):
														_UpperCAmelCase	     =      jnp.array([timesteps]    ,		dtype=jnp.intaa      )
								elif isinstance(__A    ,		jnp.ndarray      ) and len(timesteps.shape      ) == 0:
														_UpperCAmelCase	     =      timesteps.astype(dtype=jnp.floataa      )
														_UpperCAmelCase	     =      jnp.expand_dims(__A    ,		0      )

								_UpperCAmelCase	     =      self.time_proj(__A      )
								_UpperCAmelCase	     =      self.time_embedding(__A      )

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

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

								if down_block_additional_residuals is not None:
														_UpperCAmelCase	     =      ()

														for down_block_res_sample, down_block_additional_residual in zip(
														    __A    ,		__A      ):
																				down_block_res_sample += down_block_additional_residual
																				new_down_block_res_samples += (down_block_res_sample,)

														_UpperCAmelCase	     =      new_down_block_res_samples

								# 4. mid
								_UpperCAmelCase	     =      self.mid_block(__A    ,		__A    ,		__A    ,		deterministic=not train      )

								if mid_block_additional_residual is not None:
														sample += mid_block_additional_residual

								# 5. up
								for up_block in self.up_blocks:
														_UpperCAmelCase	     =      down_block_res_samples[-(self.layers_per_block + 1) :]
														_UpperCAmelCase	     =      down_block_res_samples[: -(self.layers_per_block + 1)]
														if isinstance(__A    ,		__A      ):
																				_UpperCAmelCase	     =      up_block(
																				    __A    ,		temb=__A    ,		encoder_hidden_states=__A    ,		res_hidden_states_tuple=__A    ,		deterministic=not train    ,		)
														else:
																				_UpperCAmelCase	     =      up_block(__A    ,		temb=__A    ,		res_hidden_states_tuple=__A    ,		deterministic=not train      )

        # 6. post-process
								_UpperCAmelCase	     =      self.conv_norm_out(__A      )
								_UpperCAmelCase	     =      nn.silu(__A      )
								_UpperCAmelCase	     =      self.conv_out(__A      )
								_UpperCAmelCase	     =      jnp.transpose(__A    ,		(0, 3, 1, 2)      )

								if not return_dict:
														return (sample,)

								return FlaxUNetaDConditionOutput(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.'''
)





