






'''simple docstring'''



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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			42





class UpperCAmelCase_					(    __A				,       __A				):




      """simple docstring"""




      @register_to_config
      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		int = 6_5536     ,      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] = (32, 32, 64)     ,      UpperCAmelCase			:		str = None     ,      UpperCAmelCase			:		int = 8     ,      UpperCAmelCase			:		int = 1     ,      UpperCAmelCase			:		bool = False     ,      )  ->  Tuple:


       '''simple docstring'''

       super().__init__()
       lowercase					:   List[str]				  =sample_size

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

       if use_timestep_embedding:
        lowercase					:   Optional[int]				  =block_out_channels[0] * 4
        lowercase					:   Optional[int]				  =TimestepEmbedding(
            in_channels=UpperCAmelCase     ,      time_embed_dim=UpperCAmelCase     ,      act_fn=UpperCAmelCase     ,      out_dim=block_out_channels[0]     ,      )

       lowercase					:   Dict				  =nn.ModuleList([]		)
       lowercase					:   Any				  =None
       lowercase					:   Tuple				  =nn.ModuleList([]		)
       lowercase					:   int				  =None

       # down
       lowercase					:   Union[str, Any]				  =in_channels
       for i, down_block_type in enumerate(UpperCAmelCase		):
        lowercase					:   Tuple				  =output_channel
        lowercase					:   Optional[Any]				  =block_out_channels[i]

        if i == 0:
         input_channel += extra_in_channels

        lowercase					:   Union[str, Any]				  =i == len(UpperCAmelCase		) - 1

        lowercase					:   Union[str, Any]				  =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
       lowercase					:   List[Any]				  =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
       lowercase					:   List[Any]				  =list(reversed(UpperCAmelCase		)		)
       lowercase					:   Tuple				  =reversed_block_out_channels[0]
       if out_block_type is None:
        lowercase					:   str				  =out_channels
       else:
        lowercase					:   Any				  =block_out_channels[0]

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

        lowercase					:   Dict				  =i == len(UpperCAmelCase		) - 1

        lowercase					:   Union[str, Any]				  =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		)
        lowercase					:   List[str]				  =output_channel

       # out
       lowercase					:   List[Any]				  =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,      32		)
       lowercase					:   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 				A__      ( self			:		Any     ,      UpperCAmelCase			:		torch.FloatTensor     ,      UpperCAmelCase			:		Union[torch.Tensor, float, int]     ,      UpperCAmelCase			:		bool = True     ,      )  ->  Union[UNetaDOutput, Tuple]:


       '''simple docstring'''

       lowercase					:   str				  =timestep
       if not torch.is_tensor(UpperCAmelCase		):
        lowercase					:   Optional[Any]				  =torch.tensor([timesteps]     ,      dtype=torch.long     ,      device=sample.device		)
       elif torch.is_tensor(UpperCAmelCase		) and len(timesteps.shape		) == 0:
        lowercase					:   int				  =timesteps[None].to(sample.device		)

       lowercase					:   int				  =self.time_proj(UpperCAmelCase		)
       if self.config.use_timestep_embedding:
        lowercase					:   str				  =self.time_mlp(UpperCAmelCase		)
       else:
        lowercase					:   Any				  =timestep_embed[..., None]
        lowercase					:   Union[str, Any]				  =timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
        lowercase					:   str				  =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

       # 2. down
       lowercase					:   Tuple				  =()
       for downsample_block in self.down_blocks:
        lowercase  ,		lowercase					:   str				  =downsample_block(hidden_states=UpperCAmelCase     ,      temb=UpperCAmelCase		)
        down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
        lowercase					:   Union[str, Any]				  =self.mid_block(UpperCAmelCase     ,      UpperCAmelCase		)

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

       # 5. post-process
       if self.out_block:
        lowercase					:   str				  =self.out_block(UpperCAmelCase     ,      UpperCAmelCase		)

       if not return_dict:
        return (sample,)

       return UNetaDOutput(sample=UpperCAmelCase		)











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



