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      __lowercase					(    _A    ):
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__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							,							)							->		str:


									'''simple docstring'''

									super().__init__()
									lowercase							=		sample_size

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

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

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

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

												if i == 0:
															input_channel += extra_in_channels

												lowercase							=		i == len(__lowerCamelCase   ) - 1

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

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

												lowercase							=		i == len(__lowerCamelCase   ) - 1

												lowercase							=		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   )
												lowercase							=		output_channel

									# out
									lowercase							=		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,							32   )
									lowercase							=		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        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      torch.FloatTensor							,							__lowerCamelCase				:      Union[torch.Tensor, float, int]							,							__lowerCamelCase				:      bool = True							,							)							->		Union[UNetaDOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		timestep
									if not torch.is_tensor(__lowerCamelCase   ):
												lowercase							=		torch.tensor([timesteps]							,							dtype=torch.long							,							device=sample.device   )
									elif torch.is_tensor(__lowerCamelCase   ) and len(timesteps.shape   ) == 0:
												lowercase							=		timesteps[None].to(sample.device   )

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

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

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

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

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

									if not return_dict:
												return (sample,)

									return UNetaDOutput(sample=__lowerCamelCase   )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





