code: 
  
  
  
  
  
  
  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_     (		SCREAMING_SNAKE_CASE__		):
  					__lowerCamelCase										=     42
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		SCREAMING_SNAKE_CASE__       ,			SCREAMING_SNAKE_CASE__		):
  
  
  
  
  					@register_to_config
  					def __init__(							self  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = 3  ,	_lowerCAmelCase = 3  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = "positional"  ,	_lowerCAmelCase = 0  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")  ,	_lowerCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")  ,	_lowerCAmelCase = (224, 448, 672, 896)  ,	_lowerCAmelCase = 2  ,	_lowerCAmelCase = 1  ,	_lowerCAmelCase = 1  ,	_lowerCAmelCase = "conv"  ,	_lowerCAmelCase = "conv"  ,	_lowerCAmelCase = "silu"  ,	_lowerCAmelCase = 8  ,	_lowerCAmelCase = 32  ,	_lowerCAmelCase = 1e-5  ,	_lowerCAmelCase = "default"  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	):
  										super().__init__()
  
  										UpperCAmelCase__			:     Optional[Any]										= sample_size
  										UpperCAmelCase__			:     Tuple										= 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
  										UpperCAmelCase__			:     List[str]										= nn.Convad(_lowerCAmelCase  ,	block_out_channels[0]  ,	kernel_size=3  ,	padding=(1, 1)		)
  
  										# time
  										if time_embedding_type == "fourier":
  															UpperCAmelCase__			:     List[Any]										= GaussianFourierProjection(embedding_size=block_out_channels[0]  ,	scale=16		)
  															UpperCAmelCase__			:     Any										= 2 * block_out_channels[0]
  										elif time_embedding_type == "positional":
  															UpperCAmelCase__			:     Any										= Timesteps(block_out_channels[0]  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  															UpperCAmelCase__			:     List[str]										= block_out_channels[0]
  
  										UpperCAmelCase__			:     Dict										= TimestepEmbedding(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  										# class embedding
  										if class_embed_type is None and num_class_embeds is not None:
  															UpperCAmelCase__			:     Optional[Any]										= nn.Embedding(_lowerCAmelCase  ,	_lowerCAmelCase		)
  										elif class_embed_type == "timestep":
  															UpperCAmelCase__			:     Any										= TimestepEmbedding(_lowerCAmelCase  ,	_lowerCAmelCase		)
  										elif class_embed_type == "identity":
  															UpperCAmelCase__			:     Tuple										= nn.Identity(_lowerCAmelCase  ,	_lowerCAmelCase		)
  										else:
  															UpperCAmelCase__			:     int										= None
  
  										UpperCAmelCase__			:     List[Any]										= nn.ModuleList([]		)
  										UpperCAmelCase__			:     Optional[int]										= None
  										UpperCAmelCase__			:     Optional[Any]										= nn.ModuleList([]		)
  
  										# down
  										UpperCAmelCase__			:     Union[str, Any]										= block_out_channels[0]
  										for i, down_block_type in enumerate(_lowerCAmelCase		):
  															UpperCAmelCase__			:     Dict										= output_channel
  															UpperCAmelCase__			:     Union[str, Any]										= block_out_channels[i]
  															UpperCAmelCase__			:     Tuple										= i == len(_lowerCAmelCase		) - 1
  
  															UpperCAmelCase__			:     str										= 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
  										UpperCAmelCase__			:     Optional[int]										= 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
  										UpperCAmelCase__			:     List[Any]										= list(reversed(_lowerCAmelCase		)		)
  										UpperCAmelCase__			:     int										= reversed_block_out_channels[0]
  										for i, up_block_type in enumerate(_lowerCAmelCase		):
  															UpperCAmelCase__			:     Dict										= output_channel
  															UpperCAmelCase__			:     Union[str, Any]										= reversed_block_out_channels[i]
  															UpperCAmelCase__			:     Optional[int]										= reversed_block_out_channels[min(i + 1  ,	len(_lowerCAmelCase		) - 1		)]
  
  															UpperCAmelCase__			:     int										= i == len(_lowerCAmelCase		) - 1
  
  															UpperCAmelCase__			:     Tuple										= 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		)
  															UpperCAmelCase__			:     str										= output_channel
  
  										# out
  										UpperCAmelCase__			:     Tuple										= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,	32		)
  										UpperCAmelCase__			:     Optional[Any]										= nn.GroupNorm(num_channels=block_out_channels[0]  ,	num_groups=_lowerCAmelCase  ,	eps=_lowerCAmelCase		)
  										UpperCAmelCase__			:     List[Any]										= nn.SiLU()
  										UpperCAmelCase__			:     int										= nn.Convad(block_out_channels[0]  ,	_lowerCAmelCase  ,	kernel_size=3  ,	padding=1		)
  
  
  
  
  					def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = True  ,	):
  
  
  
  										if self.config.center_input_sample:
  															UpperCAmelCase__			:     Union[str, Any]										= 2 * sample - 1.0
  
  										# 1. time
  										UpperCAmelCase__			:     Any										= timestep
  										if not torch.is_tensor(_lowerCAmelCase		):
  															UpperCAmelCase__			:     Optional[int]										= torch.tensor([timesteps]  ,	dtype=torch.long  ,	device=sample.device		)
  										elif torch.is_tensor(_lowerCAmelCase		) and len(timesteps.shape		) == 0:
  															UpperCAmelCase__			:     Union[str, Any]										= timesteps[None].to(sample.device		)
  
  										# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  										UpperCAmelCase__			:     List[str]										= timesteps * torch.ones(sample.shape[0]  ,	dtype=timesteps.dtype  ,	device=timesteps.device		)
  
  										UpperCAmelCase__			:     int										= 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.
  										UpperCAmelCase__			:     Union[str, Any]										= t_emb.to(dtype=self.dtype		)
  										UpperCAmelCase__			:     str										= 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":
  																				UpperCAmelCase__			:     Optional[int]										= self.time_proj(_lowerCAmelCase		)
  
  															UpperCAmelCase__			:     int										= self.class_embedding(_lowerCAmelCase		).to(dtype=self.dtype		)
  															UpperCAmelCase__			:     str										= emb + class_emb
  
  										# 2. pre-process
  										UpperCAmelCase__			:     List[str]										= sample
  										UpperCAmelCase__			:     Tuple										= self.conv_in(_lowerCAmelCase		)
  
  										# 3. down
  										UpperCAmelCase__			:     str										= (sample,)
  										for downsample_block in self.down_blocks:
  															if hasattr(_lowerCAmelCase  ,	"""skip_conv"""		):
  																				UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			:     List[Any]										= downsample_block(
  																				    hidden_states=_lowerCAmelCase  ,	temb=_lowerCAmelCase  ,	skip_sample=_lowerCAmelCase		)
  															else:
  																				UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= downsample_block(hidden_states=_lowerCAmelCase  ,	temb=_lowerCAmelCase		)
  
  															down_block_res_samples += res_samples
  
  										# 4. mid
  										UpperCAmelCase__			:     str										= self.mid_block(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  										# 5. up
  										UpperCAmelCase__			:     Any										= None
  										for upsample_block in self.up_blocks:
  															UpperCAmelCase__			:     Any										= down_block_res_samples[-len(upsample_block.resnets		) :]
  															UpperCAmelCase__			:     str										= down_block_res_samples[: -len(upsample_block.resnets		)]
  
  															if hasattr(_lowerCAmelCase  ,	"""skip_conv"""		):
  																				UpperCAmelCase__			,		UpperCAmelCase__			:     str										= upsample_block(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  															else:
  																				UpperCAmelCase__			:     List[Any]										= upsample_block(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
          # 6. post-process
  										UpperCAmelCase__			:     Optional[Any]										= self.conv_norm_out(_lowerCAmelCase		)
  										UpperCAmelCase__			:     str										= self.conv_act(_lowerCAmelCase		)
  										UpperCAmelCase__			:     List[Any]										= self.conv_out(_lowerCAmelCase		)
  
  										if skip_sample is not None:
  															sample += skip_sample
  
  										if self.config.time_embedding_type == "fourier":
  															UpperCAmelCase__			:     Tuple										= timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]		)))		)
  															UpperCAmelCase__			:     List[str]										= sample / timesteps
  
  										if not return_dict:
  															return (sample,)
  
  										return UNetaDOutput(sample=_lowerCAmelCase		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from .stack import Stack
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Stack[str]										= Stack()
       UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
       for bracket in parentheses:
            if bracket in bracket_pairs:
                 stack.push(__lowerCamelCase					)
            elif bracket in (")", "]", "}"):
                 if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                      return False
       return stack.is_empty()
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
        SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
        print("""Balanced parentheses demonstration:\n""")
        for example in examples:
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
              print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  
  