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 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       torch.FloatTensor
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a     ,     _a    ):
  
  
  
  
      @register_to_config
      def __init__(					self  :   int      ,      lowerCAmelCase  :   Optional[Union[int, Tuple[int, int]]] = None      ,      lowerCAmelCase  :   int = 3      ,      lowerCAmelCase  :   int = 3      ,      lowerCAmelCase  :   bool = False      ,      lowerCAmelCase  :   str = "positional"      ,      lowerCAmelCase  :   int = 0      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")      ,      lowerCAmelCase  :   Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")      ,      lowerCAmelCase  :   Tuple[int] = (224, 448, 672, 896)      ,      lowerCAmelCase  :   int = 2      ,      lowerCAmelCase  :   float = 1      ,      lowerCAmelCase  :   int = 1      ,      lowerCAmelCase  :   str = "conv"      ,      lowerCAmelCase  :   str = "conv"      ,      lowerCAmelCase  :   str = "silu"      ,      lowerCAmelCase  :   Optional[int] = 8      ,      lowerCAmelCase  :   int = 32      ,      lowerCAmelCase  :   float = 1e-5      ,      lowerCAmelCase  :   str = "default"      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Optional[str] = None      ,      lowerCAmelCase  :   Optional[int] = None      ,      )	-> List[Any]:
        super().__init__()
  
        a__     		=     sample_size
        a__     		=     block_out_channels[0] * 4
  
        # Check inputs
        if len(lowerCAmelCase   ) != len(lowerCAmelCase   ):
          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(lowerCAmelCase   ) != len(lowerCAmelCase   ):
          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
        a__     		=     nn.Convad(lowerCAmelCase      ,      block_out_channels[0]      ,      kernel_size=3      ,      padding=(1, 1)   )
  
        # time
        if time_embedding_type == "fourier":
          a__     		=     GaussianFourierProjection(embedding_size=block_out_channels[0]      ,      scale=16   )
          a__     		=     2 * block_out_channels[0]
        elif time_embedding_type == "positional":
          a__     		=     Timesteps(block_out_channels[0]      ,      lowerCAmelCase      ,      lowerCAmelCase   )
          a__     		=     block_out_channels[0]
  
        a__     		=     TimestepEmbedding(lowerCAmelCase      ,      lowerCAmelCase   )
  
        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
          a__     		=     nn.Embedding(lowerCAmelCase      ,      lowerCAmelCase   )
        elif class_embed_type == "timestep":
          a__     		=     TimestepEmbedding(lowerCAmelCase      ,      lowerCAmelCase   )
        elif class_embed_type == "identity":
          a__     		=     nn.Identity(lowerCAmelCase      ,      lowerCAmelCase   )
        else:
          a__     		=     None
  
        a__     		=     nn.ModuleList([]   )
        a__     		=     None
        a__     		=     nn.ModuleList([]   )
  
        # down
        a__     		=     block_out_channels[0]
        for i, down_block_type in enumerate(lowerCAmelCase   ):
          a__     		=     output_channel
          a__     		=     block_out_channels[i]
          a__     		=     i == len(lowerCAmelCase   ) - 1
  
          a__     		=     get_down_block(
              lowerCAmelCase      ,      num_layers=lowerCAmelCase      ,      in_channels=lowerCAmelCase      ,      out_channels=lowerCAmelCase      ,      temb_channels=lowerCAmelCase      ,      add_downsample=not is_final_block      ,      resnet_eps=lowerCAmelCase      ,      resnet_act_fn=lowerCAmelCase      ,      resnet_groups=lowerCAmelCase      ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,      downsample_padding=lowerCAmelCase      ,      resnet_time_scale_shift=lowerCAmelCase      ,      downsample_type=lowerCAmelCase      ,      )
          self.down_blocks.append(lowerCAmelCase   )
  
        # mid
        a__     		=     UNetMidBlockaD(
            in_channels=block_out_channels[-1]      ,      temb_channels=lowerCAmelCase      ,      resnet_eps=lowerCAmelCase      ,      resnet_act_fn=lowerCAmelCase      ,      output_scale_factor=lowerCAmelCase      ,      resnet_time_scale_shift=lowerCAmelCase      ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]      ,      resnet_groups=lowerCAmelCase      ,      add_attention=lowerCAmelCase      ,      )
  
        # up
        a__     		=     list(reversed(lowerCAmelCase   )   )
        a__     		=     reversed_block_out_channels[0]
        for i, up_block_type in enumerate(lowerCAmelCase   ):
          a__     		=     output_channel
          a__     		=     reversed_block_out_channels[i]
          a__     		=     reversed_block_out_channels[min(i + 1      ,      len(lowerCAmelCase   ) - 1   )]
  
          a__     		=     i == len(lowerCAmelCase   ) - 1
  
          a__     		=     get_up_block(
              lowerCAmelCase      ,      num_layers=layers_per_block + 1      ,      in_channels=lowerCAmelCase      ,      out_channels=lowerCAmelCase      ,      prev_output_channel=lowerCAmelCase      ,      temb_channels=lowerCAmelCase      ,      add_upsample=not is_final_block      ,      resnet_eps=lowerCAmelCase      ,      resnet_act_fn=lowerCAmelCase      ,      resnet_groups=lowerCAmelCase      ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,      resnet_time_scale_shift=lowerCAmelCase      ,      upsample_type=lowerCAmelCase      ,      )
          self.up_blocks.append(lowerCAmelCase   )
          a__     		=     output_channel
  
        # out
        a__     		=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,      32   )
        a__     		=     nn.GroupNorm(num_channels=block_out_channels[0]      ,      num_groups=lowerCAmelCase      ,      eps=lowerCAmelCase   )
        a__     		=     nn.SiLU()
        a__     		=     nn.Convad(block_out_channels[0]      ,      lowerCAmelCase      ,      kernel_size=3      ,      padding=1   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]      ,      lowerCAmelCase  :   torch.FloatTensor      ,      lowerCAmelCase  :   Union[torch.Tensor, float, int]      ,      lowerCAmelCase  :   Optional[torch.Tensor] = None      ,      lowerCAmelCase  :   bool = True      ,      )	-> Union[UNetaDOutput, Tuple]:
        # 0. center input if necessary
        if self.config.center_input_sample:
          a__     		=     2 * sample - 1.0
  
        # 1. time
        a__     		=     timestep
        if not torch.is_tensor(lowerCAmelCase   ):
          a__     		=     torch.tensor([timesteps]      ,      dtype=torch.long      ,      device=sample.device   )
        elif torch.is_tensor(lowerCAmelCase   ) and len(timesteps.shape   ) == 0:
          a__     		=     timesteps[None].to(sample.device   )
  
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        a__     		=     timesteps * torch.ones(sample.shape[0]      ,      dtype=timesteps.dtype      ,      device=timesteps.device   )
  
        a__     		=     self.time_proj(lowerCAmelCase   )
  
        # 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.
        a__     		=     t_emb.to(dtype=self.dtype   )
        a__     		=     self.time_embedding(lowerCAmelCase   )
  
        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":
            a__     		=     self.time_proj(lowerCAmelCase   )
  
          a__     		=     self.class_embedding(lowerCAmelCase   ).to(dtype=self.dtype   )
          a__     		=     emb + class_emb
  
        # 2. pre-process
        a__     		=     sample
        a__     		=     self.conv_in(lowerCAmelCase   )
  
        # 3. down
        a__     		=     (sample,)
        for downsample_block in self.down_blocks:
          if hasattr(lowerCAmelCase      ,      "skip_conv"   ):
            a__		,a__		,a__     		=     downsample_block(
                hidden_states=lowerCAmelCase      ,      temb=lowerCAmelCase      ,      skip_sample=lowerCAmelCase   )
          else:
            a__		,a__     		=     downsample_block(hidden_states=lowerCAmelCase      ,      temb=lowerCAmelCase   )
  
          down_block_res_samples += res_samples
  
        # 4. mid
        a__     		=     self.mid_block(lowerCAmelCase      ,      lowerCAmelCase   )
  
        # 5. up
        a__     		=     None
        for upsample_block in self.up_blocks:
          a__     		=     down_block_res_samples[-len(upsample_block.resnets   ) :]
          a__     		=     down_block_res_samples[: -len(upsample_block.resnets   )]
  
          if hasattr(lowerCAmelCase      ,      "skip_conv"   ):
            a__		,a__     		=     upsample_block(lowerCAmelCase      ,      lowerCAmelCase      ,      lowerCAmelCase      ,      lowerCAmelCase   )
          else:
            a__     		=     upsample_block(lowerCAmelCase      ,      lowerCAmelCase      ,      lowerCAmelCase   )
  
          # 6. post-process
        a__     		=     self.conv_norm_out(lowerCAmelCase   )
        a__     		=     self.conv_act(lowerCAmelCase   )
        a__     		=     self.conv_out(lowerCAmelCase   )
  
        if skip_sample is not None:
          sample += skip_sample
  
        if self.config.time_embedding_type == "fourier":
          a__     		=     timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]   )))   )
          a__     		=     sample / timesteps
  
        if not return_dict:
          return (sample,)
  
        return UNetaDOutput(sample=lowerCAmelCase   )
  
  
style_context: def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    sequence.append(lowerCamelCase__					)
  
    a__     		=     0
    while sequence[index] != target:
      index += 1
  
    sequence.pop()
  
    if index == len(lowerCamelCase__					):
      return None
  
    return index
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
       _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]
  
       _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
       _SCREAMING_SNAKE_CASE   						=    int(target_input)
       _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
       if result is not None:
            print(F"""{target} found at positions: {result}""")
       else:
            print("""Not found""")
  
  