






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 				_lowerCAmelCase(       _UpperCamelCase					):





       """simple docstring"""







       a				:		List[str]									=42







class 				_lowerCAmelCase(       _UpperCamelCase							,	_UpperCamelCase					):





       """simple docstring"""







       @register_to_config
       def __init__(					self  ,			_lowerCamelCase = 1_6  ,			_lowerCamelCase = 8_8  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = 1  ,			_lowerCamelCase = 0.0  ,			_lowerCamelCase = 3_2  ,			_lowerCamelCase = None  ,			_lowerCamelCase = False  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = "geglu"  ,			_lowerCamelCase = None  ,			_lowerCamelCase = False  ,			_lowerCamelCase = False  ,			_lowerCamelCase = False  ,			_lowerCamelCase = "layer_norm"  ,			_lowerCamelCase = True  ,			):
         super().__init__()
         UpperCamelCase_:					List[str]          =				use_linear_projection
         UpperCamelCase_:					Tuple          =				num_attention_heads
         UpperCamelCase_:					int          =				attention_head_dim
         UpperCamelCase_:					Optional[int]          =				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_:					Dict          =				(in_channels is not None) and (patch_size is None)
         UpperCamelCase_:					Optional[int]          =				num_vector_embeds is not None
         UpperCamelCase_:					int          =				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_:					str          =				(
               f'''The configuration file of this model: {self.__class__} is outdated.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'  ,			__a  ,			standard_warn=__a							)
           UpperCamelCase_:					Any          =				'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_:					Dict          =				in_channels

           UpperCamelCase_:					int          =				torch.nn.GroupNorm(num_groups=__a  ,			num_channels=__a  ,			eps=1e-6  ,			affine=__a							)
           if use_linear_projection:
             UpperCamelCase_:					Tuple          =				nn.Linear(__a  ,			__a							)
           else:
             UpperCamelCase_:					List[str]          =				nn.Convad(__a  ,			__a  ,			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_:					str          =				sample_size
           UpperCamelCase_:					str          =				sample_size
           UpperCamelCase_:					int          =				num_vector_embeds
           UpperCamelCase_:					Dict          =				self.height * self.width

           UpperCamelCase_:					List[Any]          =				ImagePositionalEmbeddings(
               num_embed=__a  ,			embed_dim=__a  ,			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_:					str          =				sample_size
           UpperCamelCase_:					Tuple          =				sample_size

           UpperCamelCase_:					Union[str, Any]          =				patch_size
           UpperCamelCase_:					Union[str, Any]          =				PatchEmbed(
               height=__a  ,			width=__a  ,			patch_size=__a  ,			in_channels=__a  ,			embed_dim=__a  ,			)

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

         # 4. Define output layers
         UpperCamelCase_:					int          =				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_:					Any          =				nn.Linear(__a  ,			__a							)
           else:
             UpperCamelCase_:					Optional[int]          =				nn.Convad(__a  ,			__a  ,			kernel_size=1  ,			stride=1  ,			padding=0							)
         elif self.is_input_vectorized:
           UpperCamelCase_:					int          =				nn.LayerNorm(__a							)
           UpperCamelCase_:					List[str]          =				nn.Linear(__a  ,			self.num_vector_embeds - 1							)
         elif self.is_input_patches:
           UpperCamelCase_:					Dict          =				nn.LayerNorm(__a  ,			elementwise_affine=__a  ,			eps=1e-6							)
           UpperCamelCase_:					Optional[Any]          =				nn.Linear(__a  ,			2 * inner_dim							)
           UpperCamelCase_:					Optional[Any]          =				nn.Linear(__a  ,			patch_size * patch_size * self.out_channels							)





       def 			_a    (					self  ,			_lowerCamelCase  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = None  ,			_lowerCamelCase = True  ,			):





         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_:					Dict          =				(1 - attention_mask.to(hidden_states.dtype							)) * -1_0_0_0_0.0
           UpperCamelCase_:					int          =				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_:					List[Any]          =				(1 - encoder_attention_mask.to(hidden_states.dtype							)) * -1_0_0_0_0.0
           UpperCamelCase_:					Optional[Any]          =				encoder_attention_mask.unsqueeze(1							)

         # 1. Input
         if self.is_input_continuous:
           UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_:					Optional[Any]          =				hidden_states.shape
           UpperCamelCase_:					Optional[Any]          =				hidden_states

           UpperCamelCase_:					List[str]          =				self.norm(__a							)
           if not self.use_linear_projection:
             UpperCamelCase_:					Any          =				self.proj_in(__a							)
             UpperCamelCase_:					List[Any]          =				hidden_states.shape[1]
             UpperCamelCase_:					Optional[int]          =				hidden_states.permute(0  ,			2  ,			3  ,			1							).reshape(__a  ,			height * width  ,			__a							)
           else:
             UpperCamelCase_:					Union[str, Any]          =				hidden_states.shape[1]
             UpperCamelCase_:					List[Any]          =				hidden_states.permute(0  ,			2  ,			3  ,			1							).reshape(__a  ,			height * width  ,			__a							)
             UpperCamelCase_:					Optional[Any]          =				self.proj_in(__a							)
         elif self.is_input_vectorized:
           UpperCamelCase_:					Dict          =				self.latent_image_embedding(__a							)
         elif self.is_input_patches:
           UpperCamelCase_:					List[str]          =				self.pos_embed(__a							)

         # 2. Blocks
         for block in self.transformer_blocks:
           UpperCamelCase_:					Optional[Any]          =				block(
               __a  ,			attention_mask=__a  ,			encoder_hidden_states=__a  ,			encoder_attention_mask=__a  ,			timestep=__a  ,			cross_attention_kwargs=__a  ,			class_labels=__a  ,			)

         # 3. Output
         if self.is_input_continuous:
           if not self.use_linear_projection:
             UpperCamelCase_:					Any          =				hidden_states.reshape(__a  ,			__a  ,			__a  ,			__a							).permute(0  ,			3  ,			1  ,			2							).contiguous()
             UpperCamelCase_:					List[str]          =				self.proj_out(__a							)
           else:
             UpperCamelCase_:					Union[str, Any]          =				self.proj_out(__a							)
             UpperCamelCase_:					List[Any]          =				hidden_states.reshape(__a  ,			__a  ,			__a  ,			__a							).permute(0  ,			3  ,			1  ,			2							).contiguous()

           UpperCamelCase_:					Dict          =				hidden_states + residual
         elif self.is_input_vectorized:
           UpperCamelCase_:					List[str]          =				self.norm_out(__a							)
           UpperCamelCase_:					str          =				self.out(__a							)
           # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
           UpperCamelCase_:					Dict          =				logits.permute(0  ,			2  ,			1							)

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

           # unpatchify
           UpperCamelCase_:					Union[str, Any]          =				int(hidden_states.shape[1] ** 0.5							)
           UpperCamelCase_:					List[str]          =				hidden_states.reshape(
               shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)							)
           UpperCamelCase_:					Union[str, Any]          =				torch.einsum('nhwpqc->nchpwq'  ,			__a							)
           UpperCamelCase_:					List[str]          =				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=__a							)


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase			       =			{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 						UpperCamelCase_(			snake_case__:  dict[str, list[str]]   )       -> set[tuple[str, str]]:
 UpperCAmelCase__							=				{node: {node} for node in graph}

 UpperCAmelCase__							=				{node: graph[node][:] for node in graph}

 while len(snake_case__   ) > 2:
  # Choose a random edge.
  UpperCAmelCase__							=				random.choice(list(graph_copy.keys()   )   )
  UpperCAmelCase__							=				random.choice(graph_copy[u]   )

  # Contract edge (u, v) to new node uv
  UpperCAmelCase__							=				u + v
  UpperCAmelCase__							=				list(set(graph_copy[u] + graph_copy[v]   )   )
  uv_neighbors.remove(snake_case__   )
  uv_neighbors.remove(snake_case__   )
  UpperCAmelCase__							=				uv_neighbors
  for neighbor in uv_neighbors:
   graph_copy[neighbor].append(snake_case__   )

  UpperCAmelCase__							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

  # Remove nodes u and v.
  del graph_copy[u]
  del graph_copy[v]
  for neighbor in uv_neighbors:
   if u in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )
   if v in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )

    # Find cutset.
 UpperCAmelCase__							=				[contracted_nodes[node] for node in graph_copy]
 return {
     (node, neighbor)
     for node in groups[0]
     for neighbor in graph[node]
     if neighbor in groups[1]
 }


if __name__ == "__main__":
 print(partition_graph(TEST_GRAPH))



