"""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 			_lowercase		(   __lowerCamelCase  ):
				lowerCamelCase__						=       42


class 			_lowercase		(   __lowerCamelCase      ,      __lowerCamelCase  ):

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


										'''simple docstring'''
										super().__init__()

										UpperCAmelCase__						=		sample_size
										UpperCAmelCase__						=		block_out_channels[0] * 4

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

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

										UpperCAmelCase__						=		TimestepEmbedding(_lowercase       ,    _lowercase     )

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

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

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

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

										# mid
										UpperCAmelCase__						=		UNetMidBlockaD(
										    in_channels=block_out_channels[-1]       ,    temb_channels=_lowercase       ,    resnet_eps=_lowercase       ,    resnet_act_fn=_lowercase       ,    output_scale_factor=_lowercase       ,    resnet_time_scale_shift=_lowercase       ,    attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,    resnet_groups=_lowercase       ,    add_attention=_lowercase       ,    )

										# up
										UpperCAmelCase__						=		list(reversed(_lowercase     )     )
										UpperCAmelCase__						=		reversed_block_out_channels[0]
										for i, up_block_type in enumerate(_lowercase     ):
																UpperCAmelCase__						=		output_channel
																UpperCAmelCase__						=		reversed_block_out_channels[i]
																UpperCAmelCase__						=		reversed_block_out_channels[min(i + 1       ,    len(_lowercase     ) - 1     )]

																UpperCAmelCase__						=		i == len(_lowercase     ) - 1

																UpperCAmelCase__						=		get_up_block(
																    _lowercase       ,    num_layers=layers_per_block + 1       ,    in_channels=_lowercase       ,    out_channels=_lowercase       ,    prev_output_channel=_lowercase       ,    temb_channels=_lowercase       ,    add_upsample=not is_final_block       ,    resnet_eps=_lowercase       ,    resnet_act_fn=_lowercase       ,    resnet_groups=_lowercase       ,    attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,    resnet_time_scale_shift=_lowercase       ,    upsample_type=_lowercase       ,    )
																self.up_blocks.append(_lowercase     )
																UpperCAmelCase__						=		output_channel

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

				def        A (     self       :    Union[str, Any]       ,    _lowercase       :    torch.FloatTensor       ,    _lowercase       :    Union[torch.Tensor, float, int]       ,    _lowercase       :    Optional[torch.Tensor] = None       ,    _lowercase       :    bool = True       ,    ):


										'''simple docstring'''
										# 0. center input if necessary
										if self.config.center_input_sample:
																UpperCAmelCase__						=		2 * sample - 1.0

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

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

										UpperCAmelCase__						=		self.time_proj(_lowercase     )

										# 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__						=		t_emb.to(dtype=self.dtype     )
										UpperCAmelCase__						=		self.time_embedding(_lowercase     )

										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__						=		self.time_proj(_lowercase     )

																UpperCAmelCase__						=		self.class_embedding(_lowercase     ).to(dtype=self.dtype     )
																UpperCAmelCase__						=		emb + class_emb

										# 2. pre-process
										UpperCAmelCase__						=		sample
										UpperCAmelCase__						=		self.conv_in(_lowercase     )

										# 3. down
										UpperCAmelCase__						=		(sample,)
										for downsample_block in self.down_blocks:
																if hasattr(_lowercase       ,    """skip_conv"""     ):
																						UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		downsample_block(
																						    hidden_states=_lowercase       ,    temb=_lowercase       ,    skip_sample=_lowercase     )
																else:
																						UpperCAmelCase__	,      UpperCAmelCase__						=		downsample_block(hidden_states=_lowercase       ,    temb=_lowercase     )

																down_block_res_samples += res_samples

										# 4. mid
										UpperCAmelCase__						=		self.mid_block(_lowercase       ,    _lowercase     )

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

																if hasattr(_lowercase       ,    """skip_conv"""     ):
																						UpperCAmelCase__	,      UpperCAmelCase__						=		upsample_block(_lowercase       ,    _lowercase       ,    _lowercase       ,    _lowercase     )
																else:
																						UpperCAmelCase__						=		upsample_block(_lowercase       ,    _lowercase       ,    _lowercase     )

        # 6. post-process
										UpperCAmelCase__						=		self.conv_norm_out(_lowercase     )
										UpperCAmelCase__						=		self.conv_act(_lowercase     )
										UpperCAmelCase__						=		self.conv_out(_lowercase     )

										if skip_sample is not None:
																sample += skip_sample

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

										if not return_dict:
																return (sample,)

										return UNetaDOutput(sample=_lowercase     )


"""simple docstring"""







from itertools import combinations_with_replacement


def   a__      (		UpperCAmelCase__       :			int = 1_0_0			)->	int:
						UpperCAmelCase__						=		[*list(range(1		,			2_1			)			), 2_5]
						UpperCAmelCase__						=		[2 * x for x in range(1		,			2_1			)] + [5_0]
						UpperCAmelCase__						=		[3 * x for x in range(1		,			2_1			)]
						UpperCAmelCase__						=		singles + doubles + triples + [0]

						UpperCAmelCase__						=		0
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(UpperCAmelCase__		,			2			):
																		UpperCAmelCase__						=		double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
						print(F"""{solution() = }""")

