




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   A(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						42







class   A(						__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 @register_to_config
 def __init__(				self					:  str						,							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						,							)   ->	Tuple:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_		     =    sample_size

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

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

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

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

             if i == 0:
                   input_channel += extra_in_channels

             lowerCamelCase_		     =    i == len(UpperCAmelCase_  ) - 1

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

       # mid
       lowerCamelCase_		     =    get_mid_block(
           UpperCAmelCase_						,							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=UpperCAmelCase_						,							add_downsample=UpperCAmelCase_						,							)

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

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

             lowerCamelCase_		     =    i == len(UpperCAmelCase_  ) - 1

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

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





 def 				a__ (				self					:  Dict						,							A_					:  torch.FloatTensor						,							A_					:  Union[torch.Tensor, float, int]						,							A_					:  bool = True						,							)   ->	Union[UNetaDOutput, Tuple]:

       """simple docstring"""






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

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

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

       # 3. mid
       if self.mid_block:
             lowerCamelCase_		     =    self.mid_block(UpperCAmelCase_						,							UpperCAmelCase_  )

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

       # 5. post-process
       if self.out_block:
             lowerCamelCase_		     =    self.out_block(UpperCAmelCase_						,							UpperCAmelCase_  )

       if not return_dict:
             return (sample,)

       return UNetaDOutput(sample=UpperCAmelCase_  )










import logging

from transformers.configuration_utils import PretrainedConfig


__A									=				logging.getLogger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"masked_bert"

 def __init__(self				:   Dict       , UpperCAmelCase_				:   Any=30_522       , UpperCAmelCase_				:   List[Any]=768       , UpperCAmelCase_				:   Optional[Any]=12       , UpperCAmelCase_				:   str=12       , UpperCAmelCase_				:   Tuple=3_072       , UpperCAmelCase_				:   str="gelu"       , UpperCAmelCase_				:   Any=0.1       , UpperCAmelCase_				:   Tuple=0.1       , UpperCAmelCase_				:   Optional[Any]=512       , UpperCAmelCase_				:   Union[str, Any]=2       , UpperCAmelCase_				:   str=0.02       , UpperCAmelCase_				:   str=1E-1_2       , UpperCAmelCase_				:   Union[str, Any]=0       , UpperCAmelCase_				:   str="topK"       , UpperCAmelCase_				:   List[str]="constant"       , UpperCAmelCase_				:   str=0.0       , **UpperCAmelCase_				:   int       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_       , **UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =vocab_size
   lowerCamelCase__:      Dict			  =hidden_size
   lowerCamelCase__:      Optional[int]			  =num_hidden_layers
   lowerCamelCase__:      Any			  =num_attention_heads
   lowerCamelCase__:      List[Any]			  =hidden_act
   lowerCamelCase__:      str			  =intermediate_size
   lowerCamelCase__:      Dict			  =hidden_dropout_prob
   lowerCamelCase__:      str			  =attention_probs_dropout_prob
   lowerCamelCase__:      int			  =max_position_embeddings
   lowerCamelCase__:      Tuple			  =type_vocab_size
   lowerCamelCase__:      str			  =initializer_range
   lowerCamelCase__:      List[Any]			  =layer_norm_eps
   lowerCamelCase__:      str			  =pruning_method
   lowerCamelCase__:      Union[str, Any]			  =mask_init
   lowerCamelCase__:      Optional[Any]			  =mask_scale

