




"""simple docstring"""
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a						,	__a=0.0						,	__a = None						,	__a = "geglu"						,	__a = None						,	__a = False						,	__a = False						,	__a = False						,	__a = False						,	__a = True						,	__a = "layer_norm"						,	__a = False						,	):
								super().__init__()
								__lowerCamelCase					:	List[str]	       =				only_cross_attention

								__lowerCamelCase					:	str	       =				(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
								__lowerCamelCase					:	Tuple	       =				(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

								if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
											raise ValueError(
											    f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
											    f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''     )

								# Define 3 blocks. Each block has its own normalization layer.
								# 1. Self-Attn
								if self.use_ada_layer_norm:
											__lowerCamelCase					:	List[Any]	       =				AdaLayerNorm(__a						,	__a     )
								elif self.use_ada_layer_norm_zero:
											__lowerCamelCase					:	int	       =				AdaLayerNormZero(__a						,	__a     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				nn.LayerNorm(__a						,	elementwise_affine=__a     )
								__lowerCamelCase					:	Optional[Any]	       =				Attention(
								    query_dim=__a						,	heads=__a						,	dim_head=__a						,	dropout=__a						,	bias=__a						,	cross_attention_dim=cross_attention_dim if only_cross_attention else None						,	upcast_attention=__a						,	)

								# 2. Cross-Attn
								if cross_attention_dim is not None or double_self_attention:
											# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
											# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
											# the second cross attention block.
											__lowerCamelCase					:	List[str]	       =				(
											    AdaLayerNorm(__a						,	__a     )
											    if self.use_ada_layer_norm
											    else nn.LayerNorm(__a						,	elementwise_affine=__a     )
											)
											__lowerCamelCase					:	str	       =				Attention(
											    query_dim=__a						,	cross_attention_dim=cross_attention_dim if not double_self_attention else None						,	heads=__a						,	dim_head=__a						,	dropout=__a						,	bias=__a						,	upcast_attention=__a						,	)  # is self-attn if encoder_hidden_states is none
								else:
											__lowerCamelCase					:	Optional[Any]	       =				None
											__lowerCamelCase					:	List[str]	       =				None

								# 3. Feed-forward
								__lowerCamelCase					:	Optional[Any]	       =				nn.LayerNorm(__a						,	elementwise_affine=__a     )
								__lowerCamelCase					:	Optional[Any]	       =				FeedForward(__a						,	dropout=__a						,	activation_fn=__a						,	final_dropout=__a     )

								# let chunk size default to None
								__lowerCamelCase					:	Any	       =				None
								__lowerCamelCase					:	Tuple	       =				0
					def     snake_case_				(    self						,	__a						,	__a     ):
								# Sets chunk feed-forward
								__lowerCamelCase					:	str	       =				chunk_size
								__lowerCamelCase					:	Union[str, Any]	       =				dim





					def     snake_case_				(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	):
								# Notice that normalization is always applied before the real computation in the following blocks.
								# 1. Self-Attention
								if self.use_ada_layer_norm:
											__lowerCamelCase					:	Optional[Any]	       =				self.norma(__a						,	__a     )
								elif self.use_ada_layer_norm_zero:
											__lowerCamelCase					:	Optional[Any]	       =				self.norma(
											    __a						,	__a						,	__a						,	hidden_dtype=hidden_states.dtype     )
								else:
											__lowerCamelCase					:	List[Any]	       =				self.norma(__a     )

								__lowerCamelCase					:	Optional[int]	       =				cross_attention_kwargs if cross_attention_kwargs is not None else {}

								__lowerCamelCase					:	int	       =				self.attna(
								    __a						,	encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None						,	attention_mask=__a						,	**__a						,	)
								if self.use_ada_layer_norm_zero:
											__lowerCamelCase					:	Tuple	       =				gate_msa.unsqueeze(1     ) * attn_output
								__lowerCamelCase					:	Any	       =				attn_output + hidden_states

								# 2. Cross-Attention
								if self.attna is not None:
											__lowerCamelCase					:	Optional[int]	       =				(
											    self.norma(__a						,	__a     ) if self.use_ada_layer_norm else self.norma(__a     )
											)

											__lowerCamelCase					:	Union[str, Any]	       =				self.attna(
											    __a						,	encoder_hidden_states=__a						,	attention_mask=__a						,	**__a						,	)
											__lowerCamelCase					:	Any	       =				attn_output + hidden_states

								# 3. Feed-forward
								__lowerCamelCase					:	Any	       =				self.norma(__a     )

								if self.use_ada_layer_norm_zero:
											__lowerCamelCase					:	List[str]	       =				norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

								if self._chunk_size is not None:
											# "feed_forward_chunk_size" can be used to save memory
											if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
														raise ValueError(
														    f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''     )

											__lowerCamelCase					:	Optional[int]	       =				norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
											__lowerCamelCase					:	int	       =				torch.cat(
											    [self.ff(__a     ) for hid_slice in norm_hidden_states.chunk(__a						,	dim=self._chunk_dim     )]						,	dim=self._chunk_dim						,	)
								else:
											__lowerCamelCase					:	str	       =				self.ff(__a     )

								if self.use_ada_layer_norm_zero:
											__lowerCamelCase					:	str	       =				gate_mlp.unsqueeze(1     ) * ff_output

								__lowerCamelCase					:	Any	       =				ff_output + hidden_states

								return hidden_states



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = 4						,	__a = 0.0						,	__a = "geglu"						,	__a = False						,	):
								super().__init__()
								__lowerCamelCase					:	Optional[Any]	       =				int(dim * mult     )
								__lowerCamelCase					:	Any	       =				dim_out if dim_out is not None else dim

								if activation_fn == "gelu":
											__lowerCamelCase					:	List[Any]	       =				GELU(__a						,	__a     )
								if activation_fn == "gelu-approximate":
											__lowerCamelCase					:	Optional[Any]	       =				GELU(__a						,	__a						,	approximate='tanh'     )
								elif activation_fn == "geglu":
											__lowerCamelCase					:	Optional[int]	       =				GEGLU(__a						,	__a     )
								elif activation_fn == "geglu-approximate":
											__lowerCamelCase					:	Any	       =				ApproximateGELU(__a						,	__a     )

								__lowerCamelCase					:	List[Any]	       =				nn.ModuleList([]     )
								# project in
								self.net.append(__a     )
								# project dropout
								self.net.append(nn.Dropout(__a     )     )
								# project out
								self.net.append(nn.Linear(__a						,	__a     )     )
								# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
								if final_dropout:
											self.net.append(nn.Dropout(__a     )     )





					def     snake_case_				(    self						,	__a     ):
								for module in self.net:
											__lowerCamelCase					:	Optional[Any]	       =				module(__a     )
								return hidden_states



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = "none"     ):
								super().__init__()
								__lowerCamelCase					:	Optional[int]	       =				nn.Linear(__a						,	__a     )
								__lowerCamelCase					:	List[Any]	       =				approximate
					def     snake_case_				(    self						,	__a     ):
								if gate.device.type != "mps":
											return F.gelu(__a						,	approximate=self.approximate     )
								# mps: gelu is not implemented for float16
								return F.gelu(gate.to(dtype=torch.floataa     )						,	approximate=self.approximate     ).to(dtype=gate.dtype     )





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Any	       =				self.proj(__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.gelu(__a     )
								return hidden_states



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	Any	       =				nn.Linear(__a						,	dim_out * 2     )
					def     snake_case_				(    self						,	__a     ):
								if gate.device.type != "mps":
											return F.gelu(__a     )
								# mps: gelu is not implemented for float16
								return F.gelu(gate.to(dtype=torch.floataa     )     ).to(dtype=gate.dtype     )





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	str	       =				self.proj(__a     ).chunk(2						,	dim=-1     )
								return hidden_states * self.gelu(__a     )



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	List[Any]	       =				nn.Linear(__a						,	__a     )





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	int	       =				self.proj(__a     )
								return x * torch.sigmoid(1.702 * x     )



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	Any	       =				nn.Embedding(__a						,	__a     )
								__lowerCamelCase					:	List[Any]	       =				nn.SiLU()
								__lowerCamelCase					:	Union[str, Any]	       =				nn.Linear(__a						,	embedding_dim * 2     )
								__lowerCamelCase					:	Union[str, Any]	       =				nn.LayerNorm(__a						,	elementwise_affine=__a     )





					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.linear(self.silu(self.emb(__a     )     )     )
								__lowerCamelCase					:	int	       =				torch.chunk(__a						,	2     )
								__lowerCamelCase					:	Optional[int]	       =				self.norm(__a     ) * (1 + scale) + shift
								return x



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a     ):
								super().__init__()

								__lowerCamelCase					:	Union[str, Any]	       =				CombinedTimestepLabelEmbeddings(__a						,	__a     )

								__lowerCamelCase					:	List[Any]	       =				nn.SiLU()
								__lowerCamelCase					:	Optional[int]	       =				nn.Linear(__a						,	6 * embedding_dim						,	bias=__a     )
								__lowerCamelCase					:	Any	       =				nn.LayerNorm(__a						,	elementwise_affine=__a						,	eps=1E-6     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a=None     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.linear(self.silu(self.emb(__a						,	__a						,	hidden_dtype=__a     )     )     )
								__lowerCamelCase					:	List[str]	       =				emb.chunk(6						,	dim=1     )
								__lowerCamelCase					:	Dict	       =				self.norm(__a     ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
								return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a						,	__a = None						,	__a = 1E-5     ):
								super().__init__()
								__lowerCamelCase					:	Tuple	       =				num_groups
								__lowerCamelCase					:	Tuple	       =				eps

								if act_fn is None:
											__lowerCamelCase					:	Union[str, Any]	       =				None
								else:
											__lowerCamelCase					:	List[str]	       =				get_activation(__a     )

								__lowerCamelCase					:	Optional[int]	       =				nn.Linear(__a						,	out_dim * 2     )





					def     snake_case_				(    self						,	__a						,	__a     ):
								if self.act:
											__lowerCamelCase					:	Union[str, Any]	       =				self.act(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				self.linear(__a     )
								__lowerCamelCase					:	Tuple	       =				emb[:, :, None, None]
								__lowerCamelCase					:	str	       =				emb.chunk(2						,	dim=1     )

								__lowerCamelCase					:	Dict	       =				F.group_norm(__a						,	self.num_groups						,	eps=self.eps     )
								__lowerCamelCase					:	Optional[int]	       =				x * (1 + scale) + shift
								return x









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



