


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):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ (       _a			,_a):




						@register_to_config
						def __init__(	self						:   Union[str, 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__()
									a__ :str						  =    sample_size

									# time
									if time_embedding_type == "fourier":
												a__ :Any						  =    GaussianFourierProjection(
												    embedding_size=8   ,     set_W_to_weight=__A   ,     log=__A   ,     flip_sin_to_cos=__A						)
												a__ :Dict						  =    2 * block_out_channels[0]
									elif time_embedding_type == "positional":
												a__ :Optional[int]						  =    Timesteps(
												    block_out_channels[0]   ,     flip_sin_to_cos=__A   ,     downscale_freq_shift=__A						)
												a__ :List[str]						  =    block_out_channels[0]

									if use_timestep_embedding:
												a__ :Tuple						  =    block_out_channels[0] * 4
												a__ :Union[str, Any]						  =    TimestepEmbedding(
												    in_channels=__A   ,     time_embed_dim=__A   ,     act_fn=__A   ,     out_dim=block_out_channels[0]   ,     )

									a__ :Optional[int]						  =    nn.ModuleList([]						)
									a__ :str						  =    None
									a__ :List[str]						  =    nn.ModuleList([]						)
									a__ :Optional[int]						  =    None

									# down
									a__ :Optional[Any]						  =    in_channels
									for i, down_block_type in enumerate(__A						):
												a__ :Optional[Any]						  =    output_channel
												a__ :List[Any]						  =    block_out_channels[i]

												if i == 0:
															input_channel += extra_in_channels

												a__ :Optional[Any]						  =    i == len(__A						) - 1

												a__ :List[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
									a__ :Union[str, Any]						  =    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
									a__ :Tuple						  =    list(reversed(__A						)						)
									a__ :Optional[Any]						  =    reversed_block_out_channels[0]
									if out_block_type is None:
												a__ :Any						  =    out_channels
									else:
												a__ :List[Any]						  =    block_out_channels[0]

									for i, up_block_type in enumerate(__A						):
												a__ :Any						  =    output_channel
												a__ :Tuple						  =    (
												    reversed_block_out_channels[i + 1] if i < len(__A						) - 1 else final_upsample_channels
												)

												a__ :str						  =    i == len(__A						) - 1

												a__ :List[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						)
												a__ :Dict						  =    output_channel

									# out
									a__ :int						  =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,     32						)
									a__ :Dict						  =    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  _snake_case					(	self						:   Tuple   ,     __A						:   torch.FloatTensor   ,     __A						:   Union[torch.Tensor, float, int]   ,     __A						:   bool = True   ,     )      ->Union[UNetaDOutput, Tuple]:






									"""simple docstring"""






									a__ :Optional[int]						  =    timestep
									if not torch.is_tensor(__A						):
												a__ :str						  =    torch.tensor([timesteps]   ,     dtype=torch.long   ,     device=sample.device						)
									elif torch.is_tensor(__A						) and len(timesteps.shape						) == 0:
												a__ :Dict						  =    timesteps[None].to(sample.device						)

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

									# 2. down
									a__ :Optional[int]						  =    ()
									for downsample_block in self.down_blocks:
												a__  ,  a__ :List[str]						  =    downsample_block(hidden_states=__A   ,     temb=__A						)
												down_block_res_samples += res_samples

									# 3. mid
									if self.mid_block:
												a__ :Optional[int]						  =    self.mid_block(__A   ,     __A						)

									# 4. up
									for i, upsample_block in enumerate(self.up_blocks						):
												a__ :List[str]						  =    down_block_res_samples[-1:]
												a__ :Optional[Any]						  =    down_block_res_samples[:-1]
												a__ :List[str]						  =    upsample_block(__A   ,     res_hidden_states_tuple=__A   ,     temb=__A						)

									# 5. post-process
									if self.out_block:
												a__ :Tuple						  =    self.out_block(__A   ,     __A						)

									if not return_dict:
												return (sample,)

									return UNetaDOutput(sample=__A						)






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *


