code: 
  
  
  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 UNetMidBlockaD, get_down_block, get_up_block
  
  @dataclass
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
  
  
  
  
  
  
  class 						A__      (					snake_case__ ,      snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      @register_to_config
      def __init__( self    ,      __snake_case = None    ,      __snake_case = 3    ,      __snake_case = 3    ,      __snake_case = False    ,      __snake_case = "positional"    ,      __snake_case = 0    ,      __snake_case = True    ,      __snake_case = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")    ,      __snake_case = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")    ,      __snake_case = (2_2_4, 4_4_8, 6_7_2, 8_9_6)    ,      __snake_case = 2    ,      __snake_case = 1    ,      __snake_case = 1    ,      __snake_case = "conv"    ,      __snake_case = "conv"    ,      __snake_case = "silu"    ,      __snake_case = 8    ,      __snake_case = 3_2    ,      __snake_case = 1E-5    ,      __snake_case = "default"    ,      __snake_case = True    ,      __snake_case = None    ,      __snake_case = None    ,      ):
           super().__init__()
  
           snake_case					=       sample_size
           snake_case					=       block_out_channels[0] * 4
  
           # Check inputs
           if len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''					)
  
           if len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''					)
  
           # input
           snake_case					=       nn.Convad(__snake_case    ,      block_out_channels[0]    ,      kernel_size=3    ,      padding=(1, 1)					)
  
           # time
           if time_embedding_type == "fourier":
                snake_case					=       GaussianFourierProjection(embedding_size=block_out_channels[0]    ,      scale=1_6					)
                snake_case					=       2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                snake_case					=       Timesteps(block_out_channels[0]    ,      __snake_case    ,      __snake_case					)
                snake_case					=       block_out_channels[0]
  
           snake_case					=       TimestepEmbedding(__snake_case    ,      __snake_case					)
  
           # class embedding
           if class_embed_type is None and num_class_embeds is not None:
                snake_case					=       nn.Embedding(__snake_case    ,      __snake_case					)
           elif class_embed_type == "timestep":
                snake_case					=       TimestepEmbedding(__snake_case    ,      __snake_case					)
           elif class_embed_type == "identity":
                snake_case					=       nn.Identity(__snake_case    ,      __snake_case					)
           else:
                snake_case					=       None
  
           snake_case					=       nn.ModuleList([]					)
           snake_case					=       None
           snake_case					=       nn.ModuleList([]					)
  
           # down
           snake_case					=       block_out_channels[0]
           for i, down_block_type in enumerate(__snake_case					):
                snake_case					=       output_channel
                snake_case					=       block_out_channels[i]
                snake_case					=       i == len(__snake_case					) - 1
  
                snake_case					=       get_down_block(
                    __snake_case    ,      num_layers=__snake_case    ,      in_channels=__snake_case    ,      out_channels=__snake_case    ,      temb_channels=__snake_case    ,      add_downsample=not is_final_block    ,      resnet_eps=__snake_case    ,      resnet_act_fn=__snake_case    ,      resnet_groups=__snake_case    ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,      downsample_padding=__snake_case    ,      resnet_time_scale_shift=__snake_case    ,      downsample_type=__snake_case    ,      )
                self.down_blocks.append(__snake_case					)
  
           # mid
           snake_case					=       UNetMidBlockaD(
               in_channels=block_out_channels[-1]    ,      temb_channels=__snake_case    ,      resnet_eps=__snake_case    ,      resnet_act_fn=__snake_case    ,      output_scale_factor=__snake_case    ,      resnet_time_scale_shift=__snake_case    ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]    ,      resnet_groups=__snake_case    ,      add_attention=__snake_case    ,      )
  
           # up
           snake_case					=       list(reversed(__snake_case					)					)
           snake_case					=       reversed_block_out_channels[0]
           for i, up_block_type in enumerate(__snake_case					):
                snake_case					=       output_channel
                snake_case					=       reversed_block_out_channels[i]
                snake_case					=       reversed_block_out_channels[min(i + 1    ,      len(__snake_case					) - 1					)]
  
                snake_case					=       i == len(__snake_case					) - 1
  
                snake_case					=       get_up_block(
                    __snake_case    ,      num_layers=layers_per_block + 1    ,      in_channels=__snake_case    ,      out_channels=__snake_case    ,      prev_output_channel=__snake_case    ,      temb_channels=__snake_case    ,      add_upsample=not is_final_block    ,      resnet_eps=__snake_case    ,      resnet_act_fn=__snake_case    ,      resnet_groups=__snake_case    ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,      resnet_time_scale_shift=__snake_case    ,      upsample_type=__snake_case    ,      )
                self.up_blocks.append(__snake_case					)
                snake_case					=       output_channel
  
           # out
           snake_case					=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,      3_2					)
           snake_case					=       nn.GroupNorm(num_channels=block_out_channels[0]    ,      num_groups=__snake_case    ,      eps=__snake_case					)
           snake_case					=       nn.SiLU()
           snake_case					=       nn.Convad(block_out_channels[0]    ,      __snake_case    ,      kernel_size=3    ,      padding=1					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = None    ,      __snake_case = True    ,      ):
           # 0. center input if necessary
           if self.config.center_input_sample:
                snake_case					=       2 * sample - 1.0
  
           # 1. time
           snake_case					=       timestep
           if not torch.is_tensor(__snake_case					):
                snake_case					=       torch.tensor([timesteps]    ,      dtype=torch.long    ,      device=sample.device					)
           elif torch.is_tensor(__snake_case					) and len(timesteps.shape					) == 0:
                snake_case					=       timesteps[None].to(sample.device					)
  
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           snake_case					=       timesteps * torch.ones(sample.shape[0]    ,      dtype=timesteps.dtype    ,      device=timesteps.device					)
  
           snake_case					=       self.time_proj(__snake_case					)
  
           # timesteps does not contain any weights and will always return f32 tensors
           # but time_embedding might actually be running in fp16. so we need to cast here.
           # there might be better ways to encapsulate this.
           snake_case					=       t_emb.to(dtype=self.dtype					)
           snake_case					=       self.time_embedding(__snake_case					)
  
           if self.class_embedding is not None:
                if class_labels is None:
                     raise ValueError('''class_labels should be provided when doing class conditioning'''					)
  
                if self.config.class_embed_type == "timestep":
                     snake_case					=       self.time_proj(__snake_case					)
  
                snake_case					=       self.class_embedding(__snake_case					).to(dtype=self.dtype					)
                snake_case					=       emb + class_emb
  
           # 2. pre-process
           snake_case					=       sample
           snake_case					=       self.conv_in(__snake_case					)
  
           # 3. down
           snake_case					=       (sample,)
           for downsample_block in self.down_blocks:
                if hasattr(__snake_case    ,      '''skip_conv'''					):
                     snake_case				,						snake_case				,						snake_case					=       downsample_block(
                         hidden_states=__snake_case    ,      temb=__snake_case    ,      skip_sample=__snake_case					)
                else:
                     snake_case				,						snake_case					=       downsample_block(hidden_states=__snake_case    ,      temb=__snake_case					)
  
                down_block_res_samples += res_samples
  
           # 4. mid
           snake_case					=       self.mid_block(__snake_case    ,      __snake_case					)
  
           # 5. up
           snake_case					=       None
           for upsample_block in self.up_blocks:
                snake_case					=       down_block_res_samples[-len(upsample_block.resnets					) :]
                snake_case					=       down_block_res_samples[: -len(upsample_block.resnets					)]
  
                if hasattr(__snake_case    ,      '''skip_conv'''					):
                     snake_case				,						snake_case					=       upsample_block(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
                else:
                     snake_case					=       upsample_block(__snake_case    ,      __snake_case    ,      __snake_case					)
  
          # 6. post-process
           snake_case					=       self.conv_norm_out(__snake_case					)
           snake_case					=       self.conv_act(__snake_case					)
           snake_case					=       self.conv_out(__snake_case					)
  
           if skip_sample is not None:
                sample += skip_sample
  
           if self.config.time_embedding_type == "fourier":
                snake_case					=       timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
                snake_case					=       sample / timesteps
  
           if not return_dict:
                return (sample,)
  
           return UNetaDOutput(sample=__snake_case					)
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  