


'''simple docstring'''

from queue import PriorityQueue
from typing import Any

import numpy as np





def __snake_case					(      UpperCAmelCase_						:						dict					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						set					,				UpperCAmelCase_						:						set					,				UpperCAmelCase_						:						dict					,				UpperCAmelCase_						:						dict					,				UpperCAmelCase_						:						PriorityQueue					,				UpperCAmelCase_						:						dict					,				UpperCAmelCase_						:						float | int					,				):
 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowerCamelCase_      =  cst_fwd.get(UpperCAmelCase_					,				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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						dict					,				UpperCAmelCase_						:						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(UpperCAmelCase_		)

  lowerCamelCase_ ,lowerCamelCase_      =  queue_backward.get()
  visited_backward.add(UpperCAmelCase_		)

  lowerCamelCase_      =  pass_and_relaxation(
      UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				)

  lowerCamelCase_      =  pass_and_relaxation(
      UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				)

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

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


a_  :				List[Any]						= {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
a_  :				Optional[int]						= {
    """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()








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 SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = "layer_norm",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       Dict          =   only_cross_attention

        A :       Tuple          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
        A :       Union[str, 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:
            A :       Dict          =   AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A :       List[str]          =   AdaLayerNormZero(lowerCamelCase__,		lowerCamelCase__		)
        else:
            A :       Tuple          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       Any          =   Attention(
            query_dim=lowerCamelCase__,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if only_cross_attention else None,		upcast_attention=lowerCamelCase__,		)

        # 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.
            A :       int          =   (
                AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
                if self.use_ada_layer_norm
                else nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
            )
            A :       Dict          =   Attention(
                query_dim=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if not double_self_attention else None,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		upcast_attention=lowerCamelCase__,		)  # is self-attn if encoder_hidden_states is none
        else:
            A :       Dict          =   None
            A :       Dict          =   None

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

        # let chunk size default to None
        A :       Optional[Any]          =   None
        A :       int          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        # Sets chunk feed-forward
        A :       List[str]          =   chunk_size
        A :       int          =   dim






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
            A :       Optional[int]          =   self.norma(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A     ,							A     ,							A     ,							A     ,							A :       Tuple          =   self.norma(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=hidden_states.dtype		)
        else:
            A :       Tuple          =   self.norma(lowerCamelCase__		)

        A :       Dict          =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

        A :       str          =   self.attna(
            lowerCamelCase__,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        if self.use_ada_layer_norm_zero:
            A :       List[Any]          =   gate_msa.unsqueeze(1		) * attn_output
        A :       str          =   attn_output + hidden_states

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

            A :       Optional[Any]          =   self.attna(
                lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
            A :       Dict          =   attn_output + hidden_states

        # 3. Feed-forward
        A :       str          =   self.norma(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Tuple          =   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`.'''		)

            A :       Any          =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            A :       Optional[Any]          =   torch.cat(
                [self.ff(lowerCamelCase__		) for hid_slice in norm_hidden_states.chunk(lowerCamelCase__,		dim=self._chunk_dim		)],		dim=self._chunk_dim,		)
        else:
            A :       Dict          =   self.ff(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Optional[Any]          =   gate_mlp.unsqueeze(1		) * ff_output

        A :       Optional[int]          =   ff_output + hidden_states

        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






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

        if activation_fn == "gelu":
            A :       Dict          =   GELU(lowerCamelCase__,		lowerCamelCase__		)
        if activation_fn == "gelu-approximate":
            A :       Optional[int]          =   GELU(lowerCamelCase__,		lowerCamelCase__,		approximate="""tanh"""		)
        elif activation_fn == "geglu":
            A :       Any          =   GEGLU(lowerCamelCase__,		lowerCamelCase__		)
        elif activation_fn == "geglu-approximate":
            A :       Optional[Any]          =   ApproximateGELU(lowerCamelCase__,		lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        for module in self.net:
            A :       int          =   module(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = "none"		):
        super().__init__()
        A :       Optional[int]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)
        A :       int          =   approximate






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__,		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 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        A :       Union[str, Any]          =   self.gelu(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		dim_out * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       Any          =   self.proj(lowerCamelCase__		).chunk(2,		dim=-1		)
        return hidden_states * self.gelu(lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        return x * torch.sigmoid(1.702 * x		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Dict          =   nn.Embedding(lowerCamelCase__,		lowerCamelCase__		)
        A :       Tuple          =   nn.SiLU()
        A :       Tuple          =   nn.Linear(lowerCamelCase__,		embedding_dim * 2		)
        A :       List[str]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   self.linear(self.silu(self.emb(lowerCamelCase__		)		)		)
        A     ,							A :       Optional[int]          =   torch.chunk(lowerCamelCase__,		2		)
        A :       Tuple          =   self.norm(lowerCamelCase__		) * (1 + scale) + shift
        return x






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






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

        A :       Union[str, Any]          =   CombinedTimestepLabelEmbeddings(lowerCamelCase__,		lowerCamelCase__		)

        A :       List[Any]          =   nn.SiLU()
        A :       str          =   nn.Linear(lowerCamelCase__,		6 * embedding_dim,		bias=lowerCamelCase__		)
        A :       List[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__,		eps=1e-6		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Tuple          =   self.linear(self.silu(self.emb(lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=lowerCamelCase__		)		)		)
        A     ,							A     ,							A     ,							A     ,							A     ,							A :       Optional[Any]          =   emb.chunk(6,		dim=1		)
        A :       List[str]          =   self.norm(lowerCamelCase__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 1e-5		):
        super().__init__()
        A :       int          =   num_groups
        A :       Dict          =   eps

        if act_fn is None:
            A :       Union[str, Any]          =   None
        else:
            A :       Optional[int]          =   get_activation(lowerCamelCase__		)

        A :       Dict          =   nn.Linear(lowerCamelCase__,		out_dim * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self.act:
            A :       Dict          =   self.act(lowerCamelCase__		)
        A :       Optional[int]          =   self.linear(lowerCamelCase__		)
        A :       int          =   emb[:, :, None, None]
        A     ,							A :       Tuple          =   emb.chunk(2,		dim=1		)

        A :       Any          =   F.group_norm(lowerCamelCase__,		self.num_groups,		eps=self.eps		)
        A :       List[str]          =   x * (1 + scale) + shift
        return x


