


"""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 			_UpperCAmelCase (							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				,       ):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						List[Any]						=				only_cross_attention

			__snake_case	:						Union[str, Any]						=				(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
			__snake_case	:						Any						=				(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:
				__snake_case	:						Dict						=				AdaLayerNorm(a_				,       a_					)
			elif self.use_ada_layer_norm_zero:
				__snake_case	:						int						=				AdaLayerNormZero(a_				,       a_					)
			else:
				__snake_case	:						Optional[Any]						=				nn.LayerNorm(a_				,       elementwise_affine=a_					)
			__snake_case	:						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.
				__snake_case	:						Dict						=				(
				    AdaLayerNorm(a_				,       a_					)
				    if self.use_ada_layer_norm
				    else nn.LayerNorm(a_				,       elementwise_affine=a_					)
				)
				__snake_case	:						List[Any]						=				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:
				__snake_case	:						int						=				None
				__snake_case	:						Union[str, Any]						=				None

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

			# let chunk size default to None
			__snake_case	:						Tuple						=				None
			__snake_case	:						int						=				0



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				chunk_size
			__snake_case	:						Optional[Any]						=				dim



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       ):





			'''simple docstring'''

			if self.use_ada_layer_norm:
				__snake_case	:						Tuple						=				self.norma(a_				,       a_					)
			elif self.use_ada_layer_norm_zero:
				__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				self.norma(
				    a_				,       a_				,       a_				,       hidden_dtype=hidden_states.dtype					)
			else:
				__snake_case	:						Optional[Any]						=				self.norma(a_					)

			__snake_case	:						Optional[Any]						=				cross_attention_kwargs if cross_attention_kwargs is not None else {}

			__snake_case	:						List[Any]						=				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:
				__snake_case	:						Optional[Any]						=				gate_msa.unsqueeze(1					) * attn_output
			__snake_case	:						List[str]						=				attn_output + hidden_states

			# 2. Cross-Attention
			if self.attna is not None:
				__snake_case	:						List[Any]						=				(
				    self.norma(a_				,       a_					) if self.use_ada_layer_norm else self.norma(a_					)
				)

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

			# 3. Feed-forward
			__snake_case	:						Any						=				self.norma(a_					)

			if self.use_ada_layer_norm_zero:
				__snake_case	:						Optional[Any]						=				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`."""					)

				__snake_case	:						List[str]						=				norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
				__snake_case	:						Dict						=				torch.cat(
				    [self.ff(a_					) for hid_slice in norm_hidden_states.chunk(a_				,       dim=self._chunk_dim					)]				,       dim=self._chunk_dim				,       )
			else:
				__snake_case	:						Tuple						=				self.ff(a_					)

			if self.use_ada_layer_norm_zero:
				__snake_case	:						Any						=				gate_mlp.unsqueeze(1					) * ff_output

			__snake_case	:						int						=				ff_output + hidden_states

			return hidden_states



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_ = None				,       a_ = 4				,       a_ = 0.0				,       a_ = "geglu"				,       a_ = False				,       ):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Union[str, Any]						=				int(dim * mult					)
			__snake_case	:						List[Any]						=				dim_out if dim_out is not None else dim

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

			__snake_case	:						List[str]						=				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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			for module in self.net:
				__snake_case	:						List[Any]						=				module(a_					)
			return hidden_states



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_ = "none"					):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Optional[int]						=				nn.Linear(a_				,       a_					)
			__snake_case	:						Optional[Any]						=				approximate



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.proj(a_					)
			__snake_case	:						List[str]						=				self.gelu(a_					)
			return hidden_states



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Union[str, Any]						=				nn.Linear(a_				,       dim_out * 2					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Tuple						=				self.proj(a_					).chunk(2				,       dim=-1					)
			return hidden_states * self.gelu(a_					)



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Dict						=				nn.Linear(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.proj(a_					)
			return x * torch.sigmoid(1.702 * x					)



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Union[str, Any]						=				nn.Embedding(a_				,       a_					)
			__snake_case	:						Any						=				nn.SiLU()
			__snake_case	:						str						=				nn.Linear(a_				,       embedding_dim * 2					)
			__snake_case	:						Dict						=				nn.LayerNorm(a_				,       elementwise_affine=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

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



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			super().__init__()

			__snake_case	:						Optional[int]						=				CombinedTimestepLabelEmbeddings(a_				,       a_					)

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_=None					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.linear(self.silu(self.emb(a_				,       a_				,       hidden_dtype=a_					)					)					)
			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						str						=				emb.chunk(6				,       dim=1					)
			__snake_case	:						Any						=				self.norm(a_					) * (1 + scale_msa[:, None]) + shift_msa[:, None]
			return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class 			_UpperCAmelCase (							nn.Module				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_				,       a_ = None				,       a_ = 1E-5					):





			'''simple docstring'''

			super().__init__()
			__snake_case	:						Optional[Any]						=				num_groups
			__snake_case	:						Any						=				eps

			if act_fn is None:
				__snake_case	:						Optional[Any]						=				None
			else:
				__snake_case	:						Tuple						=				get_activation(a_					)

			__snake_case	:						List[str]						=				nn.Linear(a_				,       out_dim * 2					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			if self.act:
				__snake_case	:						Dict						=				self.act(a_					)
			__snake_case	:						List[str]						=				self.linear(a_					)
			__snake_case	:						Tuple						=				emb[:, :, None, None]
			__snake_case	,							__snake_case	:						Optional[int]						=				emb.chunk(2				,       dim=1					)

			__snake_case	:						Union[str, Any]						=				F.group_norm(a_				,       self.num_groups				,       eps=self.eps					)
			__snake_case	:						int						=				x * (1 + scale) + shift
			return x











from queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




