




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						(			lowercase__				):
       __a       :  torch.FloatTensor






class     _a						(			lowercase__							,							lowercase__				):
       @register_to_config
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			Dict = 32     ,       lowercase		:			str = 64     ,       lowercase		:			int = 20     ,       lowercase		:			List[Any] = 768     ,       lowercase		:			Dict=77     ,       lowercase		:			List[str]=4     ,       lowercase		:			List[Any] = 0.0     ,       lowercase		:			List[str] = "silu"     ,       lowercase		:			Dict = None     ,       lowercase		:			Union[str, Any] = None     ,       lowercase		:			Optional[Any] = "linear"     ,       lowercase		:			Optional[Any] = "prd"     ,       lowercase		:			int = None     ,       lowercase		:			Union[str, Any] = None     ,       lowercase		:			Optional[int] = None     ,       ):



           '''simple docstring'''

           super().__init__()
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	attention_head_dim
           UpperCAmelCase     =	num_attention_heads * attention_head_dim
           UpperCAmelCase     =	additional_embeddings

           UpperCAmelCase     =	time_embed_dim or inner_dim
           UpperCAmelCase     =	embedding_proj_dim or embedding_dim
           UpperCAmelCase     =	clip_embed_dim or embedding_dim

           UpperCAmelCase     =	Timesteps(lowercase     ,       lowercase     ,       0       )
           UpperCAmelCase     =	TimestepEmbedding(lowercase     ,       lowercase     ,       out_dim=lowercase     ,       act_fn=lowercase       )

           UpperCAmelCase     =	nn.Linear(lowercase     ,       lowercase       )

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

           UpperCAmelCase     =	nn.Linear(lowercase     ,       lowercase       )

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

           UpperCAmelCase     =	nn.Parameter(torch.zeros(1     ,       num_embeddings + additional_embeddings     ,       lowercase       )       )

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

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

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

           UpperCAmelCase     =	nn.LayerNorm(lowercase       )

           UpperCAmelCase     =	nn.Linear(lowercase     ,       lowercase       )

           UpperCAmelCase     =	torch.full(
               [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]     ,       -1_0000.0       )
           causal_attention_mask.triu_(1       )
           UpperCAmelCase     =	causal_attention_mask[None, ...]
           self.register_buffer('''causal_attention_mask'''     ,       lowercase     ,       persistent=lowercase       )

           UpperCAmelCase     =	nn.Parameter(torch.zeros(1     ,       lowercase       )       )
           UpperCAmelCase     =	nn.Parameter(torch.zeros(1     ,       lowercase       )       )
       @property
       # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	{}

           def fn_recursive_add_processors(lowercase		:			Union[str, Any]     ,       lowercase		:			str     ,       lowercase		:			Dict       ):
               if hasattr(lowercase     ,       '''set_processor'''       ):
                   UpperCAmelCase     =	module.processor

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

               return processors

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

           return processors
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	len(self.attn_processors.keys()       )

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

           def fn_recursive_attn_processor(lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       lowercase		:			Tuple       ):
               if hasattr(lowercase     ,       '''set_processor'''       ):
                   if not isinstance(lowercase     ,       lowercase       ):
                       module.set_processor(lowercase       )
                   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}"     ,       lowercase     ,       lowercase       )

           for name, module in self.named_children():
               fn_recursive_attn_processor(lowercase     ,       lowercase     ,       lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           self.set_attn_processor(AttnProcessor()       )
       def 					A   (			self		:			Any     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple = None     ,       lowercase		:			Optional[int] = None     ,       lowercase		:			List[str] = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	hidden_states.shape[0]

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

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

           UpperCAmelCase     =	self.time_proj(lowercase       )

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

           if self.embedding_proj_norm is not None:
               UpperCAmelCase     =	self.embedding_proj_norm(lowercase       )

           UpperCAmelCase     =	self.embedding_proj(lowercase       )
           if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               UpperCAmelCase     =	self.encoder_hidden_states_proj(lowercase       )
           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'''       )

           UpperCAmelCase     =	self.proj_in(lowercase       )

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

           UpperCAmelCase     =	[]
           UpperCAmelCase     =	0

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

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

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

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

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

           UpperCAmelCase     =	torch.cat(
               lowercase     ,       dim=1     ,       )

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

           UpperCAmelCase     =	hidden_states + positional_embeddings

           if attention_mask is not None:
               UpperCAmelCase     =	(1 - attention_mask.to(hidden_states.dtype       )) * -1_0000.0
               UpperCAmelCase     =	F.pad(lowercase     ,       (0, self.additional_embeddings)     ,       value=0.0       )
               UpperCAmelCase     =	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype       )
               UpperCAmelCase     =	attention_mask.repeat_interleave(self.config.num_attention_heads     ,       dim=0       )

           if self.norm_in is not None:
               UpperCAmelCase     =	self.norm_in(lowercase       )

           for block in self.transformer_blocks:
               UpperCAmelCase     =	block(lowercase     ,       attention_mask=lowercase       )

           UpperCAmelCase     =	self.norm_out(lowercase       )

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

           UpperCAmelCase     =	self.proj_to_clip_embeddings(lowercase       )

           if not return_dict:
               return (predicted_image_embedding,)

           return PriorTransformerOutput(predicted_image_embedding=lowercase       )







       def 					A   (			self		:			str     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	(prior_latents * self.clip_std) + self.clip_mean
           return prior_latents










'''simple docstring'''







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int					,     _a			:							int					,     _a			:							list[int]  ):

    # 1. Validate that path exists between current and next vertices
    if graph[path[curr_ind - 1]][next_ver] == 0:
        return False

    # 2. Validate that next vertex is not already in path
    return not any(vertex == next_ver for vertex in path  )





def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							list[int]					,     _a			:							int  ):

    # Base Case
    if curr_ind == len(_a  ):
        # return whether path exists between current and starting vertices
        return graph[path[curr_ind - 1]][path[0]] == 1

    # Recursive Step
    for next_ver in range(0					,     len(_a  )  ):
        if valid_connection(_a					,     _a					,     _a					,     _a  ):
            # Insert current vertex  into path as next transition
            UpperCAmelCase     =	next_ver
            # Validate created path
            if util_hamilton_cycle(_a					,     _a					,     curr_ind + 1  ):
                return True
            # Backtrack
            UpperCAmelCase     =	-1
    return False







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int = 0  ):
    UpperCAmelCase     =	[-1] * (len(_a  ) + 1)
    # initialize start and end of path with starting index
    UpperCAmelCase     =	UpperCAmelCase     =	start_index
    # evaluate and if we find answer return path either return empty array
    return path if util_hamilton_cycle(_a					,     _a					,     1  ) else []




