





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  __UpperCamelCase						(       A__						):
     __A  :     torch.FloatTensor




class  __UpperCamelCase						(       A__  ,   A__						):





     @register_to_config
     def __init__(    self							,       _UpperCamelCase = None							,       _UpperCamelCase = 3							,       _UpperCamelCase = 3							,       _UpperCamelCase = False							,       _UpperCamelCase = "positional"							,       _UpperCamelCase = 0							,       _UpperCamelCase = True							,       _UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")							,       _UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")							,       _UpperCamelCase = (224, 448, 672, 896)							,       _UpperCamelCase = 2							,       _UpperCamelCase = 1							,       _UpperCamelCase = 1							,       _UpperCamelCase = "conv"							,       _UpperCamelCase = "conv"							,       _UpperCamelCase = "silu"							,       _UpperCamelCase = 8							,       _UpperCamelCase = 32							,       _UpperCamelCase = 1e-5							,       _UpperCamelCase = "default"							,       _UpperCamelCase = True							,       _UpperCamelCase = None							,       _UpperCamelCase = None							,       ):
        super().__init__()

        _UpperCAmelCase											=      sample_size
        _UpperCAmelCase											=      block_out_channels[0] * 4

        # Check inputs
        if len(_UpperCamelCase				) != len(_UpperCamelCase				):
           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(_UpperCamelCase				) != len(_UpperCamelCase				):
           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(_UpperCamelCase							,       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=16				)
           _UpperCAmelCase											=      2 * block_out_channels[0]
        elif time_embedding_type == "positional":
           _UpperCAmelCase											=      Timesteps(block_out_channels[0]							,       _UpperCamelCase							,       _UpperCamelCase				)
           _UpperCAmelCase											=      block_out_channels[0]

        _UpperCAmelCase											=      TimestepEmbedding(_UpperCamelCase							,       _UpperCamelCase				)

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

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

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

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

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

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

           _UpperCAmelCase											=      i == len(_UpperCamelCase				) - 1

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

        # out
        _UpperCAmelCase											=      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,       32				)
        _UpperCAmelCase											=      nn.GroupNorm(num_channels=block_out_channels[0]							,       num_groups=_UpperCamelCase							,       eps=_UpperCamelCase				)
        _UpperCAmelCase											=      nn.SiLU()
        _UpperCAmelCase											=      nn.Convad(block_out_channels[0]							,       _UpperCamelCase							,       kernel_size=3							,       padding=1				)





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase = None							,       _UpperCamelCase = True							,       ):
        # 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(_UpperCamelCase				):
           _UpperCAmelCase											=      torch.tensor([timesteps]							,       dtype=torch.long							,       device=sample.device				)
        elif torch.is_tensor(_UpperCamelCase				) 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(_UpperCamelCase				)

        # 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(_UpperCamelCase				)

        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(_UpperCamelCase				)

           _UpperCAmelCase											=      self.class_embedding(_UpperCamelCase				).to(dtype=self.dtype				)
           _UpperCAmelCase											=      emb + class_emb

        # 2. pre-process
        _UpperCAmelCase											=      sample
        _UpperCAmelCase											=      self.conv_in(_UpperCamelCase				)

        # 3. down
        _UpperCAmelCase											=      (sample,)
        for downsample_block in self.down_blocks:
           if hasattr(_UpperCamelCase							,       '''skip_conv'''				):
              _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase											=      downsample_block(
                  hidden_states=_UpperCamelCase							,       temb=_UpperCamelCase							,       skip_sample=_UpperCamelCase				)
           else:
              _UpperCAmelCase					,			_UpperCAmelCase											=      downsample_block(hidden_states=_UpperCamelCase							,       temb=_UpperCamelCase				)

           down_block_res_samples += res_samples

        # 4. mid
        _UpperCAmelCase											=      self.mid_block(_UpperCamelCase							,       _UpperCamelCase				)

        # 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(_UpperCamelCase							,       '''skip_conv'''				):
              _UpperCAmelCase					,			_UpperCAmelCase											=      upsample_block(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				)
           else:
              _UpperCAmelCase											=      upsample_block(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase				)

        # 6. post-process
        _UpperCAmelCase											=      self.conv_norm_out(_UpperCamelCase				)
        _UpperCAmelCase											=      self.conv_act(_UpperCamelCase				)
        _UpperCAmelCase											=      self.conv_out(_UpperCamelCase				)

        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=_UpperCamelCase				)






def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     List[str]:






   """simple docstring"""







   print('''\nVertex Distance'''	)
   for i in range(SCREAMING_SNAKE_CASE_	):
      if dist[i] != float('''inf'''	):
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  int(dist[i]	)							,  end='''\t'''	)
      else:
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  '''INF'''							,  end='''\t'''	)
      print()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      float('''inf'''	)
   _UpperCAmelCase											=      -1
   for i in range(SCREAMING_SNAKE_CASE_	):
      if (not vset[i]) and mdist[i] < min_val:
         _UpperCAmelCase											=      i
         _UpperCAmelCase											=      mdist[i]
   return min_ind
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      [float('''inf'''	) for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      [False for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      0.0

   for _ in range(v - 1	):
      _UpperCAmelCase											=      min_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      _UpperCAmelCase											=      True

      for i in range(SCREAMING_SNAKE_CASE_	):
         if (
             (not vset[i])
             and graph[u][i] != float('''inf'''	)
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _UpperCAmelCase											=      mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  int(input("Enter number of vertices: ").strip())
   UpperCAmelCase_											=  int(input("Enter number of edges: ").strip())

   UpperCAmelCase_											=  [[float("inf") for i in range(V)] for j in range(V)]

   for i in range(V):
      UpperCAmelCase_											=  0.0

   for i in range(E):
      print("\nEdge ", i + 1)
      UpperCAmelCase_											=  int(input("Enter source:").strip())
      UpperCAmelCase_											=  int(input("Enter destination:").strip())
      UpperCAmelCase_											=  float(input("Enter weight:").strip())
      UpperCAmelCase_											=  weight

   UpperCAmelCase_											=  int(input("\nEnter shortest path source:").strip())
   dijkstra(graph, V, gsrc)