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   )


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""")

