






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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	torch.FloatTensor







class 						lowerCAmelCase   (       __a					,				__a	):

					'''simple docstring'''

					@register_to_config
					def __init__(     self :		Any						,   __a :		int = 65536						,   __a :		Optional[int] = None						,   __a :		int = 2						,   __a :		int = 2						,   __a :		int = 0						,   __a :		str = "fourier"						,   __a :		bool = True						,   __a :		bool = False						,   __a :		float = 0.0						,   __a :		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,   __a :		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,   __a :		Tuple[str] = "UNetMidBlock1D"						,   __a :		str = None						,   __a :		Tuple[int] = (32, 32, 64)						,   __a :		str = None						,   __a :		int = 8						,   __a :		int = 1						,   __a :		bool = False						,   )	->     List[str]:


												"""simple docstring"""

												super().__init__()
												__lowercase					:							Optional[int]			=   sample_size

												# time
												if time_embedding_type == "fourier":
																			__lowercase					:							Tuple			=   GaussianFourierProjection(
																			    embedding_size=8						,   set_W_to_weight=__a						,   log=__a						,   flip_sin_to_cos=__a							)
																			__lowercase					:							str			=   2 * block_out_channels[0]
												elif time_embedding_type == "positional":
																			__lowercase					:							Dict			=   Timesteps(
																			    block_out_channels[0]						,   flip_sin_to_cos=__a						,   downscale_freq_shift=__a							)
																			__lowercase					:							Dict			=   block_out_channels[0]

												if use_timestep_embedding:
																			__lowercase					:							List[Any]			=   block_out_channels[0] * 4
																			__lowercase					:							Dict			=   TimestepEmbedding(
																			    in_channels=__a						,   time_embed_dim=__a						,   act_fn=__a						,   out_dim=block_out_channels[0]						,   )

												__lowercase					:							Any			=   nn.ModuleList([]							)
												__lowercase					:							Union[str, Any]			=   None
												__lowercase					:							str			=   nn.ModuleList([]							)
												__lowercase					:							Dict			=   None

												# down
												__lowercase					:							Dict			=   in_channels
												for i, down_block_type in enumerate(__a							):
																			__lowercase					:							Dict			=   output_channel
																			__lowercase					:							List[Any]			=   block_out_channels[i]

																			if i == 0:
																										input_channel += extra_in_channels

																			__lowercase					:							Any			=   i == len(__a							) - 1

																			__lowercase					:							Optional[Any]			=   get_down_block(
																			    __a						,   num_layers=__a						,   in_channels=__a						,   out_channels=__a						,   temb_channels=block_out_channels[0]						,   add_downsample=not is_final_block or downsample_each_block						,   )
																			self.down_blocks.append(__a							)

												# mid
												__lowercase					:							Tuple			=   get_mid_block(
												    __a						,   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=__a						,   add_downsample=__a						,   )

												# up
												__lowercase					:							List[str]			=   list(reversed(__a							)							)
												__lowercase					:							Dict			=   reversed_block_out_channels[0]
												if out_block_type is None:
																			__lowercase					:							List[Any]			=   out_channels
												else:
																			__lowercase					:							str			=   block_out_channels[0]

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

																			__lowercase					:							List[str]			=   i == len(__a							) - 1

																			__lowercase					:							Any			=   get_up_block(
																			    __a						,   num_layers=__a						,   in_channels=__a						,   out_channels=__a						,   temb_channels=block_out_channels[0]						,   add_upsample=not is_final_block						,   )
																			self.up_blocks.append(__a							)
																			__lowercase					:							Optional[int]			=   output_channel

												# out
												__lowercase					:							Union[str, Any]			=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,   32							)
												__lowercase					:							int			=   get_out_block(
												    out_block_type=__a						,   num_groups_out=__a						,   embed_dim=block_out_channels[0]						,   out_channels=__a						,   act_fn=__a						,   fc_dim=block_out_channels[-1] // 4						,   )



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		torch.FloatTensor						,   __a :		Union[torch.Tensor, float, int]						,   __a :		bool = True						,   )	->     Union[UNetaDOutput, Tuple]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   timestep
												if not torch.is_tensor(__a							):
																			__lowercase					:							Union[str, Any]			=   torch.tensor([timesteps]						,   dtype=torch.long						,   device=sample.device							)
												elif torch.is_tensor(__a							) and len(timesteps.shape							) == 0:
																			__lowercase					:							Dict			=   timesteps[None].to(sample.device							)

												__lowercase					:							int			=   self.time_proj(__a							)
												if self.config.use_timestep_embedding:
																			__lowercase					:							List[str]			=   self.time_mlp(__a							)
												else:
																			__lowercase					:							Optional[Any]			=   timestep_embed[..., None]
																			__lowercase					:							Tuple			=   timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
																			__lowercase					:							Dict			=   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

												# 2. down
												__lowercase					:							Tuple			=   ()
												for downsample_block in self.down_blocks:
																			__lowercase	,	__lowercase					:							Dict			=   downsample_block(hidden_states=__a						,   temb=__a							)
																			down_block_res_samples += res_samples

												# 3. mid
												if self.mid_block:
																			__lowercase					:							List[Any]			=   self.mid_block(__a						,   __a							)

												# 4. up
												for i, upsample_block in enumerate(self.up_blocks							):
																			__lowercase					:							Optional[Any]			=   down_block_res_samples[-1:]
																			__lowercase					:							Tuple			=   down_block_res_samples[:-1]
																			__lowercase					:							Tuple			=   upsample_block(__a						,   res_hidden_states_tuple=__a						,   temb=__a							)

												# 5. post-process
												if self.out_block:
																			__lowercase					:							Tuple			=   self.out_block(__a						,   __a							)

												if not return_dict:
																			return (sample,)

												return UNetaDOutput(sample=__a							)







# flake8: noqa
# Lint as: python3
lowerCamelCase       :       Optional[Any]        =				[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental