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					(    _A    ):
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 6_55_36							,							__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							,							)							->		str:


									'''simple docstring'''

									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        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      torch.FloatTensor							,							__lowerCamelCase				:      Union[torch.Tensor, float, int]							,							__lowerCamelCase				:      bool = True							,							)							->		Union[UNetaDOutput, Tuple]:


									'''simple docstring'''

									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   )






# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

						def __repr__(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									return self.version_str






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





