




'''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 UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 				_a   (snake_case__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[int]									=				42







class 				_a   (snake_case__,		snake_case__  ):





     '''simple docstring'''



     @register_to_config
     def __init__(							self       ,__a = None       ,__a = 3       ,__a = 3       ,__a = False       ,__a = "positional"       ,__a = 0       ,__a = True       ,__a = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,__a = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,__a = (224, 448, 672, 896)       ,__a = 2       ,__a = 1       ,__a = 1       ,__a = "conv"       ,__a = "conv"       ,__a = "silu"       ,__a = 8       ,__a = 32       ,__a = 1E-5       ,__a = "default"       ,__a = True       ,__a = None       ,__a = None       ,)			->							Optional[int]:
          super().__init__()

          snake_case :			Tuple					   = sample_size
          snake_case :			List[Any]					   = block_out_channels[0] * 4

          # Check inputs
          if len(__snake_case       ) != len(__snake_case       ):
               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(__snake_case       ) != len(__snake_case       ):
               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
          snake_case :			Dict					   = nn.Convad(__snake_case       ,block_out_channels[0]       ,kernel_size=3       ,padding=(1, 1)       )

          # time
          if time_embedding_type == "fourier":
               snake_case :			List[str]					   = GaussianFourierProjection(embedding_size=block_out_channels[0]       ,scale=16       )
               snake_case :			Optional[int]					   = 2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               snake_case :			List[str]					   = Timesteps(block_out_channels[0]       ,__snake_case       ,__snake_case       )
               snake_case :			Tuple					   = block_out_channels[0]

          snake_case :			List[str]					   = TimestepEmbedding(__snake_case       ,__snake_case       )

          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
               snake_case :			List[Any]					   = nn.Embedding(__snake_case       ,__snake_case       )
          elif class_embed_type == "timestep":
               snake_case :			Any					   = TimestepEmbedding(__snake_case       ,__snake_case       )
          elif class_embed_type == "identity":
               snake_case :			Optional[int]					   = nn.Identity(__snake_case       ,__snake_case       )
          else:
               snake_case :			Any					   = None

          snake_case :			Dict					   = nn.ModuleList([]       )
          snake_case :			str					   = None
          snake_case :			List[Any]					   = nn.ModuleList([]       )

          # down
          snake_case :			Optional[Any]					   = block_out_channels[0]
          for i, down_block_type in enumerate(__snake_case       ):
               snake_case :			Tuple					   = output_channel
               snake_case :			List[str]					   = block_out_channels[i]
               snake_case :			Dict					   = i == len(__snake_case       ) - 1

               snake_case :			Tuple					   = get_down_block(
                   __snake_case       ,num_layers=__snake_case       ,in_channels=__snake_case       ,out_channels=__snake_case       ,temb_channels=__snake_case       ,add_downsample=not is_final_block       ,resnet_eps=__snake_case       ,resnet_act_fn=__snake_case       ,resnet_groups=__snake_case       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,downsample_padding=__snake_case       ,resnet_time_scale_shift=__snake_case       ,downsample_type=__snake_case       ,)
               self.down_blocks.append(__snake_case       )

          # mid
          snake_case :			Union[str, Any]					   = UNetMidBlockaD(
              in_channels=block_out_channels[-1]       ,temb_channels=__snake_case       ,resnet_eps=__snake_case       ,resnet_act_fn=__snake_case       ,output_scale_factor=__snake_case       ,resnet_time_scale_shift=__snake_case       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,resnet_groups=__snake_case       ,add_attention=__snake_case       ,)

          # up
          snake_case :			Union[str, Any]					   = list(reversed(__snake_case       )       )
          snake_case :			Any					   = reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__snake_case       ):
               snake_case :			Dict					   = output_channel
               snake_case :			Optional[int]					   = reversed_block_out_channels[i]
               snake_case :			Union[str, Any]					   = reversed_block_out_channels[min(i + 1       ,len(__snake_case       ) - 1       )]

               snake_case :			List[Any]					   = i == len(__snake_case       ) - 1

               snake_case :			Optional[int]					   = get_up_block(
                   __snake_case       ,num_layers=layers_per_block + 1       ,in_channels=__snake_case       ,out_channels=__snake_case       ,prev_output_channel=__snake_case       ,temb_channels=__snake_case       ,add_upsample=not is_final_block       ,resnet_eps=__snake_case       ,resnet_act_fn=__snake_case       ,resnet_groups=__snake_case       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,resnet_time_scale_shift=__snake_case       ,upsample_type=__snake_case       ,)
               self.up_blocks.append(__snake_case       )
               snake_case :			str					   = output_channel

          # out
          snake_case :			Dict					   = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,32       )
          snake_case :			Dict					   = nn.GroupNorm(num_channels=block_out_channels[0]       ,num_groups=__snake_case       ,eps=__snake_case       )
          snake_case :			Optional[int]					   = nn.SiLU()
          snake_case :			List[Any]					   = nn.Convad(block_out_channels[0]       ,__snake_case       ,kernel_size=3       ,padding=1       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a = None       ,__a = True       ,)			->							List[Any]:
          # 0. center input if necessary
          if self.config.center_input_sample:
               snake_case :			List[str]					   = 2 * sample - 1.0

          # 1. time
          snake_case :			Optional[int]					   = timestep
          if not torch.is_tensor(__snake_case       ):
               snake_case :			int					   = torch.tensor([timesteps]       ,dtype=torch.long       ,device=sample.device       )
          elif torch.is_tensor(__snake_case       ) and len(timesteps.shape       ) == 0:
               snake_case :			Dict					   = timesteps[None].to(sample.device       )

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          snake_case :			List[str]					   = timesteps * torch.ones(sample.shape[0]       ,dtype=timesteps.dtype       ,device=timesteps.device       )

          snake_case :			int					   = self.time_proj(__snake_case       )

          # 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.
          snake_case :			Any					   = t_emb.to(dtype=self.dtype       )
          snake_case :			List[str]					   = self.time_embedding(__snake_case       )

          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":
                    snake_case :			Optional[Any]					   = self.time_proj(__snake_case       )

               snake_case :			str					   = self.class_embedding(__snake_case       ).to(dtype=self.dtype       )
               snake_case :			Union[str, Any]					   = emb + class_emb

          # 2. pre-process
          snake_case :			Union[str, Any]					   = sample
          snake_case :			Optional[Any]					   = self.conv_in(__snake_case       )

          # 3. down
          snake_case :			Tuple					   = (sample,)
          for downsample_block in self.down_blocks:
               if hasattr(__snake_case       ,"""skip_conv"""       ):
                    snake_case   ,		snake_case   ,		snake_case :			int					   = downsample_block(
                        hidden_states=__snake_case       ,temb=__snake_case       ,skip_sample=__snake_case       )
               else:
                    snake_case   ,		snake_case :			int					   = downsample_block(hidden_states=__snake_case       ,temb=__snake_case       )

               down_block_res_samples += res_samples

          # 4. mid
          snake_case :			Dict					   = self.mid_block(__snake_case       ,__snake_case       )

          # 5. up
          snake_case :			List[str]					   = None
          for upsample_block in self.up_blocks:
               snake_case :			List[Any]					   = down_block_res_samples[-len(upsample_block.resnets       ) :]
               snake_case :			Union[str, Any]					   = down_block_res_samples[: -len(upsample_block.resnets       )]

               if hasattr(__snake_case       ,"""skip_conv"""       ):
                    snake_case   ,		snake_case :			Optional[int]					   = upsample_block(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       )
               else:
                    snake_case :			Any					   = upsample_block(__snake_case       ,__snake_case       ,__snake_case       )

        # 6. post-process
          snake_case :			Optional[int]					   = self.conv_norm_out(__snake_case       )
          snake_case :			int					   = self.conv_act(__snake_case       )
          snake_case :			Optional[int]					   = self.conv_out(__snake_case       )

          if skip_sample is not None:
               sample += skip_sample

          if self.config.time_embedding_type == "fourier":
               snake_case :			Any					   = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]       )))       )
               snake_case :			str					   = sample / timesteps

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__snake_case       )









'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def 					lowerCamelCase__							(			__lowercase							):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__lowercase    ,      -1							)



def 					lowerCamelCase__							(			__lowercase							):
     if not balanced_parentheses(__lowercase							):
          raise ValueError("""Mismatched parentheses"""							)
     snake_case :			Stack[str]					   = Stack()
     snake_case :			List[Any]					   = []
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__lowercase							)
          elif char == "(":
               stack.push(__lowercase							)
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()							)
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__lowercase							) <= precedence(stack.peek()							):
                    postfix.append(stack.pop()							)
               stack.push(__lowercase							)
     while not stack.is_empty():
          postfix.append(stack.pop()							)
     return " ".join(__lowercase							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      lowercase			:      Tuple			=					"""a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))



