





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_		(  __UpperCAmelCase					):
					lowerCAmelCase_							=	42





class 			UpperCamelCase_		(  __UpperCAmelCase							,  __UpperCAmelCase					):




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

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

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

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

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

															if i == 0:
																				input_channel += extra_in_channels

															_snake_case									=				i == len(lowerCAmelCase_  ) - 1

															_snake_case									=				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
										_snake_case									=				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
										_snake_case									=				list(reversed(lowerCAmelCase_  )  )
										_snake_case									=				reversed_block_out_channels[0]
										if out_block_type is None:
															_snake_case									=				out_channels
										else:
															_snake_case									=				block_out_channels[0]

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

															_snake_case									=				i == len(lowerCAmelCase_  ) - 1

															_snake_case									=				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_  )
															_snake_case									=				output_channel

										# out
										_snake_case									=				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,						32  )
										_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = True				,						)		->							Union[str, Any]:
										_snake_case									=				timestep
										if not torch.is_tensor(lowerCAmelCase_  ):
															_snake_case									=				torch.tensor([timesteps]				,						dtype=torch.long				,						device=sample.device  )
										elif torch.is_tensor(lowerCAmelCase_  ) and len(timesteps.shape  ) == 0:
															_snake_case									=				timesteps[None].to(sample.device  )

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

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

										# 3. mid
										if self.mid_block:
															_snake_case									=				self.mid_block(lowerCAmelCase_				,						lowerCAmelCase_  )

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

										# 5. post-process
										if self.out_block:
															_snake_case									=				self.out_block(lowerCAmelCase_				,						lowerCAmelCase_  )

										if not return_dict:
															return (sample,)

										return UNetaDOutput(sample=lowerCAmelCase_  )










from collections import deque

from .hash_table import HashTable
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Any:
										super().__init__(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				deque([]  ) if self.values[key] is None else self.values[key]
										self.values[key].appendleft(lowerCAmelCase_  )
										_snake_case									=				self.values[key]




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										return (
										    sum(self.charge_factor - len(lowerCAmelCase_  ) for slot in self.values  )
										    / self.size_table
										    * self.charge_factor
										)






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=None  )		->							str:
										if not (
										    len(self.values[key]  ) == self.charge_factor and self.values.count(lowerCAmelCase_  ) == 0
										):
															return key
										return super()._collision_resolution(lowerCAmelCase_				,						lowerCAmelCase_  )



