"""simple docstring"""






from dataclasses import dataclass
from typing import Any, Dict, Optional

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin
@dataclass
class     snake_case	(			lowercase_    ):




					"""simple docstring"""

					snake_case__       =			42






class     snake_case	(			lowercase_				,   lowercase_    ):




					"""simple docstring"""

					@register_to_config
					def __init__(    self       :     Any   ,lowerCamelCase__       :     int = 16   ,lowerCamelCase__       :     int = 88   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     int = 1   ,lowerCamelCase__       :     float = 0.0   ,lowerCamelCase__       :     int = 32   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     str = "geglu"   ,lowerCamelCase__       :     Optional[int] = None   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     str = "layer_norm"   ,lowerCamelCase__       :     bool = True   ,):
									super().__init__()
									UpperCAmelCase__					=							use_linear_projection
									UpperCAmelCase__					=							num_attention_heads
									UpperCAmelCase__					=							attention_head_dim
									UpperCAmelCase__					=							num_attention_heads * attention_head_dim

									# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
									# Define whether input is continuous or discrete depending on configuration
									UpperCAmelCase__					=							(in_channels is not None) and (patch_size is None)
									UpperCAmelCase__					=							num_vector_embeds is not None
									UpperCAmelCase__					=							in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
													UpperCAmelCase__					=							(
													    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
													    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
													    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
													    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
													    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
													)
													deprecate('norm_type!=num_embeds_ada_norm'   ,'1.0.0'   ,__UpperCamelCase   ,standard_warn=__UpperCamelCase      )
													UpperCAmelCase__					=							'ada_norm'

									if self.is_input_continuous and self.is_input_vectorized:
													raise ValueError(
													    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
													    ' sure that either `in_channels` or `num_vector_embeds` is None.'      )
									elif self.is_input_vectorized and self.is_input_patches:
													raise ValueError(
													    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
													    ' sure that either `num_vector_embeds` or `num_patches` is None.'      )
									elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
													raise ValueError(
													    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
													    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''      )

									# 2. Define input layers
									if self.is_input_continuous:
													UpperCAmelCase__					=							in_channels

													UpperCAmelCase__					=							torch.nn.GroupNorm(num_groups=__UpperCamelCase   ,num_channels=__UpperCamelCase   ,eps=1e-6   ,affine=__UpperCamelCase      )
													if use_linear_projection:
																	UpperCAmelCase__					=							nn.Linear(__UpperCamelCase   ,__UpperCamelCase      )
													else:
																	UpperCAmelCase__					=							nn.Convad(__UpperCamelCase   ,__UpperCamelCase   ,kernel_size=1   ,stride=1   ,padding=0      )
									elif self.is_input_vectorized:
													assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
													assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

													UpperCAmelCase__					=							sample_size
													UpperCAmelCase__					=							sample_size
													UpperCAmelCase__					=							num_vector_embeds
													UpperCAmelCase__					=							self.height * self.width

													UpperCAmelCase__					=							ImagePositionalEmbeddings(
													    num_embed=__UpperCamelCase   ,embed_dim=__UpperCamelCase   ,height=self.height   ,width=self.width      )
									elif self.is_input_patches:
													assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

													UpperCAmelCase__					=							sample_size
													UpperCAmelCase__					=							sample_size

													UpperCAmelCase__					=							patch_size
													UpperCAmelCase__					=							PatchEmbed(
													    height=__UpperCamelCase   ,width=__UpperCamelCase   ,patch_size=__UpperCamelCase   ,in_channels=__UpperCamelCase   ,embed_dim=__UpperCamelCase   ,)

									# 3. Define transformers blocks
									UpperCAmelCase__					=							nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            __UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,dropout=__UpperCamelCase   ,cross_attention_dim=__UpperCamelCase   ,activation_fn=__UpperCamelCase   ,num_embeds_ada_norm=__UpperCamelCase   ,attention_bias=__UpperCamelCase   ,only_cross_attention=__UpperCamelCase   ,upcast_attention=__UpperCamelCase   ,norm_type=__UpperCamelCase   ,norm_elementwise_affine=__UpperCamelCase   ,)
									        for d in range(__UpperCamelCase      )
									    ]      )

									# 4. Define output layers
									UpperCAmelCase__					=							in_channels if out_channels is None else out_channels
									if self.is_input_continuous:
													# TODO: should use out_channels for continuous projections
													if use_linear_projection:
																	UpperCAmelCase__					=							nn.Linear(__UpperCamelCase   ,__UpperCamelCase      )
													else:
																	UpperCAmelCase__					=							nn.Convad(__UpperCamelCase   ,__UpperCamelCase   ,kernel_size=1   ,stride=1   ,padding=0      )
									elif self.is_input_vectorized:
													UpperCAmelCase__					=							nn.LayerNorm(__UpperCamelCase      )
													UpperCAmelCase__					=							nn.Linear(__UpperCamelCase   ,self.num_vector_embeds - 1      )
									elif self.is_input_patches:
													UpperCAmelCase__					=							nn.LayerNorm(__UpperCamelCase   ,elementwise_affine=__UpperCamelCase   ,eps=1e-6      )
													UpperCAmelCase__					=							nn.Linear(__UpperCamelCase   ,2 * inner_dim      )
													UpperCAmelCase__					=							nn.Linear(__UpperCamelCase   ,patch_size * patch_size * self.out_channels      )







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     torch.Tensor   ,lowerCamelCase__       :     Optional[torch.Tensor] = None   ,lowerCamelCase__       :     Optional[torch.LongTensor] = None   ,lowerCamelCase__       :     Optional[torch.LongTensor] = None   ,lowerCamelCase__       :     Dict[str, Any] = None   ,lowerCamelCase__       :     Optional[torch.Tensor] = None   ,lowerCamelCase__       :     Optional[torch.Tensor] = None   ,lowerCamelCase__       :     bool = True   ,):
									# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
									#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
									#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
									# expects mask of shape:
									#   [batch, key_tokens]
									# adds singleton query_tokens dimension:
									#   [batch,                    1, key_tokens]
									# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
									#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
									#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
									if attention_mask is not None and attention_mask.ndim == 2:
													# assume that mask is expressed as:
													#   (1 = keep,      0 = discard)
													# convert mask into a bias that can be added to attention scores:
													#       (keep = +0,     discard = -10000.0)
													UpperCAmelCase__					=							(1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
													UpperCAmelCase__					=							attention_mask.unsqueeze(1      )

									# convert encoder_attention_mask to a bias the same way we do for attention_mask
									if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
													UpperCAmelCase__					=							(1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
													UpperCAmelCase__					=							encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
													UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							hidden_states.shape
													UpperCAmelCase__					=							hidden_states

													UpperCAmelCase__					=							self.norm(__UpperCamelCase      )
													if not self.use_linear_projection:
																	UpperCAmelCase__					=							self.proj_in(__UpperCamelCase      )
																	UpperCAmelCase__					=							hidden_states.shape[1]
																	UpperCAmelCase__					=							hidden_states.permute(0   ,2   ,3   ,1      ).reshape(__UpperCamelCase   ,height * width   ,__UpperCamelCase      )
													else:
																	UpperCAmelCase__					=							hidden_states.shape[1]
																	UpperCAmelCase__					=							hidden_states.permute(0   ,2   ,3   ,1      ).reshape(__UpperCamelCase   ,height * width   ,__UpperCamelCase      )
																	UpperCAmelCase__					=							self.proj_in(__UpperCamelCase      )
									elif self.is_input_vectorized:
													UpperCAmelCase__					=							self.latent_image_embedding(__UpperCamelCase      )
									elif self.is_input_patches:
													UpperCAmelCase__					=							self.pos_embed(__UpperCamelCase      )

									# 2. Blocks
									for block in self.transformer_blocks:
													UpperCAmelCase__					=							block(
													    __UpperCamelCase   ,attention_mask=__UpperCamelCase   ,encoder_hidden_states=__UpperCamelCase   ,encoder_attention_mask=__UpperCamelCase   ,timestep=__UpperCamelCase   ,cross_attention_kwargs=__UpperCamelCase   ,class_labels=__UpperCamelCase   ,)

									# 3. Output
									if self.is_input_continuous:
													if not self.use_linear_projection:
																	UpperCAmelCase__					=							hidden_states.reshape(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase      ).permute(0   ,3   ,1   ,2      ).contiguous()
																	UpperCAmelCase__					=							self.proj_out(__UpperCamelCase      )
													else:
																	UpperCAmelCase__					=							self.proj_out(__UpperCamelCase      )
																	UpperCAmelCase__					=							hidden_states.reshape(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase      ).permute(0   ,3   ,1   ,2      ).contiguous()

													UpperCAmelCase__					=							hidden_states + residual
									elif self.is_input_vectorized:
													UpperCAmelCase__					=							self.norm_out(__UpperCamelCase      )
													UpperCAmelCase__					=							self.out(__UpperCamelCase      )
													# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
													UpperCAmelCase__					=							logits.permute(0   ,2   ,1      )

													# log(p(x_0))
													UpperCAmelCase__					=							F.log_softmax(logits.double()   ,dim=1      ).float()
									elif self.is_input_patches:
													# TODO: cleanup!
													UpperCAmelCase__					=							self.transformer_blocks[0].norma.emb(
													    __UpperCamelCase   ,__UpperCamelCase   ,hidden_dtype=hidden_states.dtype      )
													UpperCAmelCase__				,     UpperCAmelCase__					=							self.proj_out_a(F.silu(__UpperCamelCase      )      ).chunk(2   ,dim=1      )
													UpperCAmelCase__					=							self.norm_out(__UpperCamelCase      ) * (1 + scale[:, None]) + shift[:, None]
													UpperCAmelCase__					=							self.proj_out_a(__UpperCamelCase      )

													# unpatchify
													UpperCAmelCase__					=							UpperCAmelCase__					=							int(hidden_states.shape[1] ** 0.5      )
													UpperCAmelCase__					=							hidden_states.reshape(
													    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
													UpperCAmelCase__					=							torch.einsum('nhwpqc->nchpwq'   ,__UpperCamelCase      )
													UpperCAmelCase__					=							hidden_states.reshape(
													    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)      )

									if not return_dict:
													return (output,)

									return TransformeraDModelOutput(sample=__UpperCamelCase      )




"""simple docstring"""






from __future__ import annotations
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Tuple   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							vertices
									UpperCAmelCase__					=							[[0] * vertices for _ in range(lowerCamelCase__      )]







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     list[int]      ):
									print('Vertex \t Distance from Source'      )
									for vertex in range(self.vertices      ):
													print(lowerCamelCase__   ,'\t\t'   ,distances_from_source[vertex]      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     list[int]   ,lowerCamelCase__       :     list[bool]      ):
									UpperCAmelCase__					=							1e7
									UpperCAmelCase__					=							0

									# Search not nearest vertex not in the shortest path tree
									for vertex in range(self.vertices      ):
													if distances_from_source[vertex] < minimum and visited[vertex] is False:
																	UpperCAmelCase__					=							distances_from_source[vertex]
																	UpperCAmelCase__					=							vertex
									return min_index







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     int      ):
									UpperCAmelCase__					=							[int(1e7      )] * self.vertices  # distances from the source
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							[False] * self.vertices

									for _ in range(self.vertices      ):
													UpperCAmelCase__					=							self.minimum_distance(lowerCamelCase__   ,lowerCamelCase__      )
													UpperCAmelCase__					=							True

													# Update dist value of the adjacent vertices
													# of the picked vertex only if the current
													# distance is greater than new distance and
													# the vertex in not in the shortest path tree
													for v in range(self.vertices      ):
																	if (
																	    self.graph[u][v] > 0
																	    and visited[v] is False
																	    and distances[v] > distances[u] + self.graph[u][v]
																	):
																					UpperCAmelCase__					=							distances[u] + self.graph[u][v]

									self.print_solution(lowerCamelCase__      )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[str]											=     Graph(9)
						lowerCAmelCase__  :  Tuple											=     [
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)



