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 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=4_2





class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ):


   @register_to_config
   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	int = 6_5536   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 0   ,		__lowerCAmelCase				:	str = "fourier"   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	float = 0.0   ,		__lowerCAmelCase				:	Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,		__lowerCAmelCase				:	Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,		__lowerCAmelCase				:	Tuple[str] = "UNetMidBlock1D"   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	Tuple[int] = (32, 32, 64)   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	int = 8   ,		__lowerCAmelCase				:	int = 1   ,		__lowerCAmelCase				:	bool = False   ,		)      ->					List[str]:
         super().__init__()
         lowercase_     =      sample_size

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

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

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

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

               if i == 0:
                     input_channel += extra_in_channels

               lowercase_     =      i == len(__lowerCAmelCase) - 1

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

         # mid
         lowercase_     =      get_mid_block(
             __lowerCAmelCase   ,		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=__lowerCAmelCase   ,		add_downsample=__lowerCAmelCase   ,		)

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

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

               lowercase_     =      i == len(__lowerCAmelCase) - 1

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

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


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	Union[torch.Tensor, float, int]   ,		__lowerCAmelCase				:	bool = True   ,		)      ->					Union[UNetaDOutput, Tuple]:
         lowercase_     =      timestep
         if not torch.is_tensor(__lowerCAmelCase):
               lowercase_     =      torch.tensor([timesteps]   ,		dtype=torch.long   ,		device=sample.device)
         elif torch.is_tensor(__lowerCAmelCase) and len(timesteps.shape) == 0:
               lowercase_     =      timesteps[None].to(sample.device)

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

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

         # 3. mid
         if self.mid_block:
               lowercase_     =      self.mid_block(__lowerCAmelCase   ,		__lowerCAmelCase)

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

         # 5. post-process
         if self.out_block:
               lowercase_     =      self.out_block(__lowerCAmelCase   ,		__lowerCAmelCase)

         if not return_dict:
               return (sample,)

         return UNetaDOutput(sample=__lowerCAmelCase)







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






