




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							torch.FloatTensor




class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase   ):
				"""simple docstring"""



				@register_to_config
				def __init__(      self				:     int					,      __lowerCamelCase				:     int = 6_55_36					,      __lowerCamelCase				:     Optional[int] = None					,      __lowerCamelCase				:     int = 2					,      __lowerCamelCase				:     int = 2					,      __lowerCamelCase				:     int = 0					,      __lowerCamelCase				:     str = "fourier"					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     float = 0.0					,      __lowerCamelCase				:     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,      __lowerCamelCase				:     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,      __lowerCamelCase				:     Tuple[str] = "UNetMidBlock1D"					,      __lowerCamelCase				:     str = None					,      __lowerCamelCase				:     Tuple[int] = (32, 32, 64)					,      __lowerCamelCase				:     str = None					,      __lowerCamelCase				:     int = 8					,      __lowerCamelCase				:     int = 1					,      __lowerCamelCase				:     bool = False					,      )					->     Tuple:
							super().__init__()
							a    =			sample_size

							# time
							if time_embedding_type == "fourier":
										a    =			GaussianFourierProjection(
										    embedding_size=8					,      set_W_to_weight=__lowerCamelCase					,      log=__lowerCamelCase					,      flip_sin_to_cos=__lowerCamelCase							)
										a    =			2 * block_out_channels[0]
							elif time_embedding_type == "positional":
										a    =			Timesteps(
										    block_out_channels[0]					,      flip_sin_to_cos=__lowerCamelCase					,      downscale_freq_shift=__lowerCamelCase							)
										a    =			block_out_channels[0]

							if use_timestep_embedding:
										a    =			block_out_channels[0] * 4
										a    =			TimestepEmbedding(
										    in_channels=__lowerCamelCase					,      time_embed_dim=__lowerCamelCase					,      act_fn=__lowerCamelCase					,      out_dim=block_out_channels[0]					,      )

							a    =			nn.ModuleList([]							)
							a    =			None
							a    =			nn.ModuleList([]							)
							a    =			None

							# down
							a    =			in_channels
							for i, down_block_type in enumerate(__lowerCamelCase							):
										a    =			output_channel
										a    =			block_out_channels[i]

										if i == 0:
													input_channel += extra_in_channels

										a    =			i == len(__lowerCamelCase							) - 1

										a    =			get_down_block(
										    __lowerCamelCase					,      num_layers=__lowerCamelCase					,      in_channels=__lowerCamelCase					,      out_channels=__lowerCamelCase					,      temb_channels=block_out_channels[0]					,      add_downsample=not is_final_block or downsample_each_block					,      )
										self.down_blocks.append(__lowerCamelCase							)

							# mid
							a    =			get_mid_block(
							    __lowerCamelCase					,      in_channels=block_out_channels[-1]					,      mid_channels=block_out_channels[-1]					,      out_channels=block_out_channels[-1]					,      embed_dim=block_out_channels[0]					,      num_layers=__lowerCamelCase					,      add_downsample=__lowerCamelCase					,      )

							# up
							a    =			list(reversed(__lowerCamelCase							)							)
							a    =			reversed_block_out_channels[0]
							if out_block_type is None:
										a    =			out_channels
							else:
										a    =			block_out_channels[0]

							for i, up_block_type in enumerate(__lowerCamelCase							):
										a    =			output_channel
										a    =			(
										    reversed_block_out_channels[i + 1] if i < len(__lowerCamelCase							) - 1 else final_upsample_channels
										)

										a    =			i == len(__lowerCamelCase							) - 1

										a    =			get_up_block(
										    __lowerCamelCase					,      num_layers=__lowerCamelCase					,      in_channels=__lowerCamelCase					,      out_channels=__lowerCamelCase					,      temb_channels=block_out_channels[0]					,      add_upsample=not is_final_block					,      )
										self.up_blocks.append(__lowerCamelCase							)
										a    =			output_channel

							# out
							a    =			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,      32							)
							a    =			get_out_block(
							    out_block_type=__lowerCamelCase					,      num_groups_out=__lowerCamelCase					,      embed_dim=block_out_channels[0]					,      out_channels=__lowerCamelCase					,      act_fn=__lowerCamelCase					,      fc_dim=block_out_channels[-1] // 4					,      )





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     torch.FloatTensor					,      __lowerCamelCase				:     Union[torch.Tensor, float, int]					,      __lowerCamelCase				:     bool = True					,      )					->     Union[UNetaDOutput, Tuple]:
							a    =			timestep
							if not torch.is_tensor(__lowerCamelCase							):
										a    =			torch.tensor([timesteps]					,      dtype=torch.long					,      device=sample.device							)
							elif torch.is_tensor(__lowerCamelCase							) and len(timesteps.shape							) == 0:
										a    =			timesteps[None].to(sample.device							)

							a    =			self.time_proj(__lowerCamelCase							)
							if self.config.use_timestep_embedding:
										a    =			self.time_mlp(__lowerCamelCase							)
							else:
										a    =			timestep_embed[..., None]
										a    =			timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
										a    =			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

							# 2. down
							a    =			()
							for downsample_block in self.down_blocks:
										a    ,		a    =			downsample_block(hidden_states=__lowerCamelCase					,      temb=__lowerCamelCase							)
										down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
										a    =			self.mid_block(__lowerCamelCase					,      __lowerCamelCase							)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks							):
										a    =			down_block_res_samples[-1:]
										a    =			down_block_res_samples[:-1]
										a    =			upsample_block(__lowerCamelCase					,      res_hidden_states_tuple=__lowerCamelCase					,      temb=__lowerCamelCase							)

							# 5. post-process
							if self.out_block:
										a    =			self.out_block(__lowerCamelCase					,      __lowerCamelCase							)

							if not return_dict:
										return (sample,)

							return UNetaDOutput(sample=__lowerCamelCase							)












import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






