




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      (		lowerCamelCase_		):
     """simple docstring"""

     _snake_case:						List[str]  =42






class       A      (		lowerCamelCase_      ,    lowerCamelCase_		):
     """simple docstring"""

     @register_to_config
     def __init__(     self	: List[str]						,	A__	: Optional[int] = None						,	A__	: str = 3						,	A__	: int = 3						,	A__	: str = False						,	A__	: Dict = "positional"						,	A__	: str = 0						,	A__	: Union[str, Any] = True						,	A__	: Optional[int] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")						,	A__	: int = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")						,	A__	: str = (2_2_4, 4_4_8, 6_7_2, 8_9_6)						,	A__	: int = 2						,	A__	: Dict = 1						,	A__	: List[str] = 1						,	A__	: Optional[Any] = "conv"						,	A__	: Optional[int] = "conv"						,	A__	: Optional[int] = "silu"						,	A__	: Dict = 8						,	A__	: Any = 3_2						,	A__	: Union[str, Any] = 1E-5						,	A__	: List[str] = "default"						,	A__	: Union[str, Any] = True						,	A__	: Tuple = None						,	A__	: Any = None						,	)					->							Optional[int]:
           super().__init__()

           lowerCamelCase__   	=					sample_size
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					nn.Convad(lowerCAmelCase_						,	block_out_channels[0]						,	kernel_size=3						,	padding=(1, 1)      )

           # time
           if time_embedding_type == "fourier":
                 lowerCamelCase__   	=					GaussianFourierProjection(embedding_size=block_out_channels[0]						,	scale=1_6      )
                 lowerCamelCase__   	=					2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 lowerCamelCase__   	=					Timesteps(block_out_channels[0]						,	lowerCAmelCase_						,	lowerCAmelCase_      )
                 lowerCamelCase__   	=					block_out_channels[0]

           lowerCamelCase__   	=					TimestepEmbedding(lowerCAmelCase_						,	lowerCAmelCase_      )

           # class embedding
           if class_embed_type is None and num_class_embeds is not None:
                 lowerCamelCase__   	=					nn.Embedding(lowerCAmelCase_						,	lowerCAmelCase_      )
           elif class_embed_type == "timestep":
                 lowerCamelCase__   	=					TimestepEmbedding(lowerCAmelCase_						,	lowerCAmelCase_      )
           elif class_embed_type == "identity":
                 lowerCamelCase__   	=					nn.Identity(lowerCAmelCase_						,	lowerCAmelCase_      )
           else:
                 lowerCamelCase__   	=					None

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

           # down
           lowerCamelCase__   	=					block_out_channels[0]
           for i, down_block_type in enumerate(lowerCAmelCase_      ):
                 lowerCamelCase__   	=					output_channel
                 lowerCamelCase__   	=					block_out_channels[i]
                 lowerCamelCase__   	=					i == len(lowerCAmelCase_      ) - 1

                 lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					list(reversed(lowerCAmelCase_      )      )
           lowerCamelCase__   	=					reversed_block_out_channels[0]
           for i, up_block_type in enumerate(lowerCAmelCase_      ):
                 lowerCamelCase__   	=					output_channel
                 lowerCamelCase__   	=					reversed_block_out_channels[i]
                 lowerCamelCase__   	=					reversed_block_out_channels[min(i + 1						,	len(lowerCAmelCase_      ) - 1      )]

                 lowerCamelCase__   	=					i == len(lowerCAmelCase_      ) - 1

                 lowerCamelCase__   	=					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_      )
                 lowerCamelCase__   	=					output_channel

           # out
           lowerCamelCase__   	=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,	3_2      )
           lowerCamelCase__   	=					nn.GroupNorm(num_channels=block_out_channels[0]						,	num_groups=lowerCAmelCase_						,	eps=lowerCAmelCase_      )
           lowerCamelCase__   	=					nn.SiLU()
           lowerCamelCase__   	=					nn.Convad(block_out_channels[0]						,	lowerCAmelCase_						,	kernel_size=3						,	padding=1      )




     def 			__snake_case (     self	: Optional[int]						,	A__	: Optional[Any]						,	A__	: int						,	A__	: Optional[int] = None						,	A__	: Union[str, Any] = True						,	)					->							Union[str, Any]:
           # 0. center input if necessary
           if self.config.center_input_sample:
                 lowerCamelCase__   	=					2 * sample - 1.0

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

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

           lowerCamelCase__   	=					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.
           lowerCamelCase__   	=					t_emb.to(dtype=self.dtype      )
           lowerCamelCase__   	=					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":
                       lowerCamelCase__   	=					self.time_proj(lowerCAmelCase_      )

                 lowerCamelCase__   	=					self.class_embedding(lowerCAmelCase_      ).to(dtype=self.dtype      )
                 lowerCamelCase__   	=					emb + class_emb

           # 2. pre-process
           lowerCamelCase__   	=					sample
           lowerCamelCase__   	=					self.conv_in(lowerCAmelCase_      )

           # 3. down
           lowerCamelCase__   	=					(sample,)
           for downsample_block in self.down_blocks:
                 if hasattr(lowerCAmelCase_						,	'skip_conv'      ):
                       lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					downsample_block(
                           hidden_states=lowerCAmelCase_						,	temb=lowerCAmelCase_						,	skip_sample=lowerCAmelCase_      )
                 else:
                       lowerCamelCase__	,		lowerCamelCase__   	=					downsample_block(hidden_states=lowerCAmelCase_						,	temb=lowerCAmelCase_      )

                 down_block_res_samples += res_samples

           # 4. mid
           lowerCamelCase__   	=					self.mid_block(lowerCAmelCase_						,	lowerCAmelCase_      )

           # 5. up
           lowerCamelCase__   	=					None
           for upsample_block in self.up_blocks:
                 lowerCamelCase__   	=					down_block_res_samples[-len(upsample_block.resnets      ) :]
                 lowerCamelCase__   	=					down_block_res_samples[: -len(upsample_block.resnets      )]

                 if hasattr(lowerCAmelCase_						,	'skip_conv'      ):
                       lowerCamelCase__	,		lowerCamelCase__   	=					upsample_block(lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_      )
                 else:
                       lowerCamelCase__   	=					upsample_block(lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_      )

        # 6. post-process
           lowerCamelCase__   	=					self.conv_norm_out(lowerCAmelCase_      )
           lowerCamelCase__   	=					self.conv_act(lowerCAmelCase_      )
           lowerCamelCase__   	=					self.conv_out(lowerCAmelCase_      )

           if skip_sample is not None:
                 sample += skip_sample

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

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=lowerCAmelCase_      )
def 	__lowerCamelCase							(				__a    : float		)      ->       float:
	return -num if num < 0 else num

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_min() arg is an empty sequence"		)
	_lowercase   =x[0]
	for i in x:
		if abs_val(__a		) < abs_val(__a		):
			_lowercase   =i
	return j

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_max() arg is an empty sequence"		)
	_lowercase   =x[0]
	for i in x:
		if abs(__a		) > abs(__a		):
			_lowercase   =i
	return j

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_max_sort() arg is an empty sequence"		)
	return sorted(__a      ,							key=__a		)[-1]

def 	__lowerCamelCase							(				)      ->       Optional[Any]:
	assert abs_val(0		) == 0
	assert abs_val(34		) == 34
	assert abs_val(-100_000_000_000		) == 100_000_000_000

	_lowercase   =[-3, -1, 2, -11]
	assert abs_max(__a		) == -11
	assert abs_max_sort(__a		) == -11
	assert abs_min(__a		) == -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						test_abs_val()
						print(abs_val(-3_4))  # --> 34




