


'''simple docstring'''






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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class  a     (     _SCREAMING_SNAKE_CASE       ):
	_lowerCAmelCase 					=	4_2





class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE       ):
	@register_to_config
	def __init__( self      ,				__magic_name__ = 32      ,				__magic_name__ = 64      ,				__magic_name__ = 20      ,				__magic_name__ = 7_68      ,				__magic_name__=77      ,				__magic_name__=4      ,				__magic_name__ = 0.0      ,				__magic_name__ = "silu"      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = "linear"      ,				__magic_name__ = "prd"      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				)					->							int:
						super().__init__()
						_a			      =							num_attention_heads
						_a			      =							attention_head_dim
						_a			      =							num_attention_heads * attention_head_dim
						_a			      =							additional_embeddings

						_a			      =							time_embed_dim or inner_dim
						_a			      =							embedding_proj_dim or embedding_dim
						_a			      =							clip_embed_dim or embedding_dim

						_a			      =							Timesteps(__magic_name__      ,				__magic_name__      ,				0						)
						_a			      =							TimestepEmbedding(__magic_name__      ,				__magic_name__      ,				out_dim=__magic_name__      ,				act_fn=__magic_name__						)

						_a			      =							nn.Linear(__magic_name__      ,				__magic_name__						)

						if embedding_proj_norm_type is None:
											_a			      =							None
						elif embedding_proj_norm_type == "layer":
											_a			      =							nn.LayerNorm(__magic_name__						)
						else:
											raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'						)

						_a			      =							nn.Linear(__magic_name__      ,				__magic_name__						)

						if encoder_hid_proj_type is None:
											_a			      =							None
						elif encoder_hid_proj_type == "linear":
											_a			      =							nn.Linear(__magic_name__      ,				__magic_name__						)
						else:
											raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'						)

						_a			      =							nn.Parameter(torch.zeros(1      ,				num_embeddings + additional_embeddings      ,				__magic_name__						)						)

						if added_emb_type == "prd":
											_a			      =							nn.Parameter(torch.zeros(1      ,				1      ,				__magic_name__						)						)
						elif added_emb_type is None:
											_a			      =							None
						else:
											raise ValueError(
											    f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'						)

						_a			      =							nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            __magic_name__      ,				__magic_name__      ,				__magic_name__      ,				dropout=__magic_name__      ,				activation_fn='gelu'      ,				attention_bias=__magic_name__      ,				)
						        for d in range(__magic_name__						)
						    ]						)

						if norm_in_type == "layer":
											_a			      =							nn.LayerNorm(__magic_name__						)
						elif norm_in_type is None:
											_a			      =							None
						else:
											raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'						)

						_a			      =							nn.LayerNorm(__magic_name__						)

						_a			      =							nn.Linear(__magic_name__      ,				__magic_name__						)

						_a			      =							torch.full(
						    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      ,				-1_00_00.0						)
						causal_attention_mask.triu_(1						)
						_a			      =							causal_attention_mask[None, ...]
						self.register_buffer('causal_attention_mask'      ,				__magic_name__      ,				persistent=__magic_name__						)

						_a			      =							nn.Parameter(torch.zeros(1      ,				__magic_name__						)						)
						_a			      =							nn.Parameter(torch.zeros(1      ,				__magic_name__						)						)
	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def 							__UpperCAmelCase		( self						)					->							Dict[str, AttentionProcessor]:
						_a			      =							{}

						def fn_recursive_add_processors(__magic_name__      ,				__magic_name__      ,				__magic_name__						):
											if hasattr(__magic_name__      ,				'set_processor'						):
																_a			      =							module.processor

											for sub_name, child in module.named_children():
																fn_recursive_add_processors(f'{name}.{sub_name}'      ,				__magic_name__      ,				__magic_name__						)

											return processors

						for name, module in self.named_children():
											fn_recursive_add_processors(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

						return processors
	def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
						_a			      =							len(self.attn_processors.keys()						)

						if isinstance(__magic_name__      ,				__magic_name__						) and len(__magic_name__						) != count:
											raise ValueError(
											    f'A dict of processors was passed, but the number of processors {len(__magic_name__						)} does not match the'
											    f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'						)

						def fn_recursive_attn_processor(__magic_name__      ,				__magic_name__      ,				__magic_name__						):
											if hasattr(__magic_name__      ,				'set_processor'						):
																if not isinstance(__magic_name__      ,				__magic_name__						):
																					module.set_processor(__magic_name__						)
																else:
																					module.set_processor(processor.pop(f'{name}.processor'						)						)

											for sub_name, child in module.named_children():
																fn_recursive_attn_processor(f'{name}.{sub_name}'      ,				__magic_name__      ,				__magic_name__						)

						for name, module in self.named_children():
											fn_recursive_attn_processor(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
						self.set_attn_processor(AttnProcessor()						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = True      ,				)					->							Any:
						_a			      =							hidden_states.shape[0]

						_a			      =							timestep
						if not torch.is_tensor(__magic_name__						):
											_a			      =							torch.tensor([timesteps]      ,				dtype=torch.long      ,				device=hidden_states.device						)
						elif torch.is_tensor(__magic_name__						) and len(timesteps.shape						) == 0:
											_a			      =							timesteps[None].to(hidden_states.device						)

						# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
						_a			      =							timesteps * torch.ones(__magic_name__      ,				dtype=timesteps.dtype      ,				device=timesteps.device						)

						_a			      =							self.time_proj(__magic_name__						)

						# timesteps does not contain any weights and will always return f32 tensors
						# but time_embedding might be fp16, so we need to cast here.
						_a			      =							timesteps_projected.to(dtype=self.dtype						)
						_a			      =							self.time_embedding(__magic_name__						)

						if self.embedding_proj_norm is not None:
											_a			      =							self.embedding_proj_norm(__magic_name__						)

						_a			      =							self.embedding_proj(__magic_name__						)
						if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
											_a			      =							self.encoder_hidden_states_proj(__magic_name__						)
						elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
											raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'						)

						_a			      =							self.proj_in(__magic_name__						)

						_a			      =							self.positional_embedding.to(hidden_states.dtype						)

						_a			      =							[]
						_a			      =							0

						if encoder_hidden_states is not None:
											additional_embeds.append(__magic_name__						)
											additional_embeddings_len += encoder_hidden_states.shape[1]

						if len(proj_embeddings.shape						) == 2:
											_a			      =							proj_embeddings[:, None, :]

						if len(hidden_states.shape						) == 2:
											_a			      =							hidden_states[:, None, :]

						_a			      =							additional_embeds + [
						    proj_embeddings,
						    time_embeddings[:, None, :],
						    hidden_states,
						]

						if self.prd_embedding is not None:
											_a			      =							self.prd_embedding.to(hidden_states.dtype						).expand(__magic_name__      ,				-1      ,				-1						)
											additional_embeds.append(__magic_name__						)

						_a			      =							torch.cat(
						    __magic_name__      ,				dim=1      ,				)

						# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
						_a			      =							additional_embeddings_len + proj_embeddings.shape[1] + 1
						if positional_embeddings.shape[1] < hidden_states.shape[1]:
											_a			      =							F.pad(
											    __magic_name__      ,				(
											        0,
											        0,
											        additional_embeddings_len,
											        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
											    )      ,				value=0.0      ,				)

						_a			      =							hidden_states + positional_embeddings

						if attention_mask is not None:
											_a			      =							(1 - attention_mask.to(hidden_states.dtype						)) * -1_00_00.0
											_a			      =							F.pad(__magic_name__      ,				(0, self.additional_embeddings)      ,				value=0.0						)
											_a			      =							(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype						)
											_a			      =							attention_mask.repeat_interleave(self.config.num_attention_heads      ,				dim=0						)

						if self.norm_in is not None:
											_a			      =							self.norm_in(__magic_name__						)

						for block in self.transformer_blocks:
											_a			      =							block(__magic_name__      ,				attention_mask=__magic_name__						)

						_a			      =							self.norm_out(__magic_name__						)

						if self.prd_embedding is not None:
											_a			      =							hidden_states[:, -1]
						else:
											_a			      =							hidden_states[:, additional_embeddings_len:]

						_a			      =							self.proj_to_clip_embeddings(__magic_name__						)

						if not return_dict:
											return (predicted_image_embedding,)

						return PriorTransformerOutput(predicted_image_embedding=__magic_name__						)




	def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
						_a			      =							(prior_latents * self.clip_std) + self.clip_mean
						return prior_latents





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :Union[str, Any]		)   ->  List[str]:




     '''simple docstring'''



     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]






def 	_A				(lowerCAmelCase__  :dict[int, list[int]]		)   ->  list[tuple[int, int]]:




     '''simple docstring'''



     _a			      =							0
     _a			      =							len(lowerCAmelCase__		)  # No of vertices in graph
     _a			      =							[0] * n
     _a			      =							[False] * n

     def dfs(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Union[str, Any]		):
          _a			      =							True
          _a			      =							id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						id_		)
                    _a			      =							min(low[at]				,						low[to]		)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)		)
               else:
                    # This edge is a back edge and cannot be a bridge
                    _a			      =							min(low[at]				,						low[to]		)

     _a			      =							[]
     for i in range(lowerCAmelCase__		):
          if not visited[i]:
               dfs(lowerCAmelCase__				,						-1				,						lowerCAmelCase__				,						id_		)
     return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

