






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							(	__snake_case     ):






  """simple docstring"""





  A_						       = 42




class     _a							(	__snake_case    ,							__snake_case     ):






  """simple docstring"""





  @register_to_config
  def __init__(  self		,		_UpperCAmelCase = 32		,		_UpperCAmelCase = 64		,		_UpperCAmelCase = 20		,		_UpperCAmelCase = 768		,		_UpperCAmelCase=77		,		_UpperCAmelCase=4		,		_UpperCAmelCase = 0.0		,		_UpperCAmelCase = "silu"		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = "linear"		,		_UpperCAmelCase = "prd"		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		)      ->			Tuple:
     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(__lowerCamelCase		,		__lowerCamelCase		,		0			)
     UpperCamelCase_						=  TimestepEmbedding(__lowerCamelCase		,		__lowerCamelCase		,		out_dim=__lowerCamelCase		,		act_fn=__lowerCamelCase			)

     UpperCamelCase_						=  nn.Linear(__lowerCamelCase		,		__lowerCamelCase			)

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

     UpperCamelCase_						=  nn.Linear(__lowerCamelCase		,		__lowerCamelCase			)

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

     UpperCamelCase_						=  nn.Parameter(torch.zeros(1		,		num_embeddings + additional_embeddings		,		__lowerCamelCase			)			)

     if added_emb_type == "prd":
        UpperCamelCase_						=  nn.Parameter(torch.zeros(1		,		1		,		__lowerCamelCase			)			)
     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(
                 __lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		dropout=__lowerCamelCase		,		activation_fn='gelu'		,		attention_bias=__lowerCamelCase		,		)
             for d in range(__lowerCamelCase			)
         ]			)

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

     UpperCamelCase_						=  nn.LayerNorm(__lowerCamelCase			)

     UpperCamelCase_						=  nn.Linear(__lowerCamelCase		,		__lowerCamelCase			)

     UpperCamelCase_						=  torch.full(
         [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]		,		-10000.0			)
     causal_attention_mask.triu_(1			)
     UpperCamelCase_						=  causal_attention_mask[None, ...]
     self.register_buffer('causal_attention_mask'		,		__lowerCamelCase		,		persistent=__lowerCamelCase			)

     UpperCamelCase_						=  nn.Parameter(torch.zeros(1		,		__lowerCamelCase			)			)
     UpperCamelCase_						=  nn.Parameter(torch.zeros(1		,		__lowerCamelCase			)			)




  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  {}

     def fn_recursive_add_processors(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        if hasattr(__lowerCamelCase		,		'set_processor'			):
           UpperCamelCase_						=  module.processor

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

        return processors

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

     return processors




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  len(self.attn_processors.keys()			)

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

     def fn_recursive_attn_processor(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        if hasattr(__lowerCamelCase		,		'set_processor'			):
           if not isinstance(__lowerCamelCase		,		__lowerCamelCase			):
              module.set_processor(__lowerCamelCase			)
           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}"""		,		__lowerCamelCase		,		__lowerCamelCase			)

     for name, module in self.named_children():
        fn_recursive_attn_processor(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     self.set_attn_processor(AttnProcessor()			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			List[Any]:
     UpperCamelCase_						=  hidden_states.shape[0]

     UpperCamelCase_						=  timestep
     if not torch.is_tensor(__lowerCamelCase			):
        UpperCamelCase_						=  torch.tensor([timesteps]		,		dtype=torch.long		,		device=hidden_states.device			)
     elif torch.is_tensor(__lowerCamelCase			) 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(__lowerCamelCase		,		dtype=timesteps.dtype		,		device=timesteps.device			)

     UpperCamelCase_						=  self.time_proj(__lowerCamelCase			)

     # 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(__lowerCamelCase			)

     if self.embedding_proj_norm is not None:
        UpperCamelCase_						=  self.embedding_proj_norm(__lowerCamelCase			)

     UpperCamelCase_						=  self.embedding_proj(__lowerCamelCase			)
     if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
        UpperCamelCase_						=  self.encoder_hidden_states_proj(__lowerCamelCase			)
     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(__lowerCamelCase			)

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

     UpperCamelCase_						=  []
     UpperCamelCase_						=  0

     if encoder_hidden_states is not None:
        additional_embeds.append(__lowerCamelCase			)
        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(__lowerCamelCase		,		-1		,		-1			)
        additional_embeds.append(__lowerCamelCase			)

     UpperCamelCase_						=  torch.cat(
         __lowerCamelCase		,		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(
            __lowerCamelCase		,		(
                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			)) * -10000.0
        UpperCamelCase_						=  F.pad(__lowerCamelCase		,		(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(__lowerCamelCase			)

     for block in self.transformer_blocks:
        UpperCamelCase_						=  block(__lowerCamelCase		,		attention_mask=__lowerCamelCase			)

     UpperCamelCase_						=  self.norm_out(__lowerCamelCase			)

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

     UpperCamelCase_						=  self.proj_to_clip_embeddings(__lowerCamelCase			)

     if not return_dict:
        return (predicted_image_embedding,)

     return PriorTransformerOutput(predicted_image_embedding=__lowerCamelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  (prior_latents * self.clip_std) + self.clip_mean
     return prior_latents








from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					Optional[int]						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__A			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

           if current_node.pos == self.target.pos:
               SCREAMING_SNAKE_CASE										=   True
               return self.retrace_path(__lowerCamelCase			)

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

       if not self.reached:
           return [self.start.pos]
       return None





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

           if grid[pos_y][pos_x] != 0:
               continue

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)