

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__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : torch.FloatTensor







class        a__       (   UpperCAmelCase	,			UpperCAmelCase       ):







  """simple docstring"""




  @register_to_config
  def __init__(			self    : Dict				,				UpperCAmelCase__    : int = 6_5_5_3_6				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : int = 2				,				UpperCAmelCase__    : int = 2				,				UpperCAmelCase__    : int = 0				,				UpperCAmelCase__    : str = "fourier"				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : float = 0.0				,				UpperCAmelCase__    : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				UpperCAmelCase__    : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				UpperCAmelCase__    : Tuple[str] = "UNetMidBlock1D"				,				UpperCAmelCase__    : str = None				,				UpperCAmelCase__    : Tuple[int] = (3_2, 3_2, 6_4)				,				UpperCAmelCase__    : str = None				,				UpperCAmelCase__    : int = 8				,				UpperCAmelCase__    : int = 1				,				UpperCAmelCase__    : bool = False				,				)					->List[str]:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE :      Dict       =   sample_size

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

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

        SCREAMING_SNAKE_CASE :      Tuple       =   nn.ModuleList([]		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   None
        SCREAMING_SNAKE_CASE :      List[str]       =   nn.ModuleList([]		)
        SCREAMING_SNAKE_CASE :      Any       =   None

        # down
        SCREAMING_SNAKE_CASE :      str       =   in_channels
        for i, down_block_type in enumerate(UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   output_channel
              SCREAMING_SNAKE_CASE :      Any       =   block_out_channels[i]

              if i == 0:
                    input_channel += extra_in_channels

              SCREAMING_SNAKE_CASE :      Optional[Any]       =   i == len(UpperCAmelCase__		) - 1

              SCREAMING_SNAKE_CASE :      Dict       =   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
        SCREAMING_SNAKE_CASE :      Dict       =   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
        SCREAMING_SNAKE_CASE :      Optional[int]       =   list(reversed(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE :      int       =   reversed_block_out_channels[0]
        if out_block_type is None:
              SCREAMING_SNAKE_CASE :      Any       =   out_channels
        else:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   block_out_channels[0]

        for i, up_block_type in enumerate(UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   output_channel
              SCREAMING_SNAKE_CASE :      str       =   (
                  reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase__		) - 1 else final_upsample_channels
              )

              SCREAMING_SNAKE_CASE :      Optional[Any]       =   i == len(UpperCAmelCase__		) - 1

              SCREAMING_SNAKE_CASE :      int       =   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__		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   output_channel

        # out
        SCREAMING_SNAKE_CASE :      Optional[int]       =   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,				3_2		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   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  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : Union[torch.Tensor, float, int]				,				UpperCAmelCase__    : bool = True				,				)					->Union[UNetaDOutput, Tuple]:
        """simple docstring"""







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

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

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

        # 3. mid
        if self.mid_block:
              SCREAMING_SNAKE_CASE :      Dict       =   self.mid_block(UpperCAmelCase__				,				UpperCAmelCase__		)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks		):
              SCREAMING_SNAKE_CASE :      Tuple       =   down_block_res_samples[-1:]
              SCREAMING_SNAKE_CASE :      List[str]       =   down_block_res_samples[:-1]
              SCREAMING_SNAKE_CASE :      List[str]       =   upsample_block(UpperCAmelCase__				,				res_hidden_states_tuple=UpperCAmelCase__				,				temb=UpperCAmelCase__		)

        # 5. post-process
        if self.out_block:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.out_block(UpperCAmelCase__				,				UpperCAmelCase__		)

        if not return_dict:
              return (sample,)

        return UNetaDOutput(sample=UpperCAmelCase__		)







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




