'''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__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     torch.FloatTensor




class     a__      (   UpperCAmelCase__							,   UpperCAmelCase__ ):






     @register_to_config
     def __init__( self		:		Tuple  ,				a		:		int = 32  ,				a		:		int = 64  ,				a		:		int = 20  ,				a		:		int = 7_68  ,				a		:		List[str]=77  ,				a		:		List[str]=4  ,				a		:		float = 0.0  ,				a		:		str = "silu"  ,				a		:		Optional[str] = None  ,				a		:		Optional[str] = None  ,				a		:		Optional[str] = "linear"  ,				a		:		Optional[str] = "prd"  ,				a		:		Optional[int] = None  ,				a		:		Optional[int] = None  ,				a		:		Optional[int] = None  ,				):





         """simple docstring"""



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

         __lowerCamelCase		=  time_embed_dim or inner_dim
         __lowerCamelCase		=  embedding_proj_dim or embedding_dim
         __lowerCamelCase		=  clip_embed_dim or embedding_dim

         __lowerCamelCase		=  Timesteps(a  ,				a  ,				0    )
         __lowerCamelCase		=  TimestepEmbedding(a  ,				a  ,				out_dim=a  ,				act_fn=a    )

         __lowerCamelCase		=  nn.Linear(a  ,				a    )

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

         __lowerCamelCase		=  nn.Linear(a  ,				a    )

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

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

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

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

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

         __lowerCamelCase		=  nn.LayerNorm(a    )

         __lowerCamelCase		=  nn.Linear(a  ,				a    )

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

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






     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  {}

         def fn_recursive_add_processors(a		:		str  ,				a		:		torch.nn.Module  ,				a		:		Dict[str, AttentionProcessor]    ):
             if hasattr(a  ,				'''set_processor'''    ):
                 __lowerCamelCase		=  module.processor

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

             return processors

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

         return processors






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]    ):





         """simple docstring"""



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

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

         def fn_recursive_attn_processor(a		:		str  ,				a		:		torch.nn.Module  ,				a		:		Dict    ):
             if hasattr(a  ,				'''set_processor'''    ):
                 if not isinstance(a  ,				a    ):
                     module.set_processor(a    )
                 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}"""  ,				a  ,				a    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         self.set_attn_processor(AttnProcessor()    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Optional[int]  ,				a		:		Union[torch.Tensor, float, int]  ,				a		:		torch.FloatTensor  ,				a		:		Optional[torch.FloatTensor] = None  ,				a		:		Optional[torch.BoolTensor] = None  ,				a		:		bool = True  ,				):





         """simple docstring"""



         __lowerCamelCase		=  hidden_states.shape[0]

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

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

         __lowerCamelCase		=  self.time_proj(a    )

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

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

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

         __lowerCamelCase		=  self.proj_in(a    )

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

         __lowerCamelCase		=  []
         __lowerCamelCase		=  0

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

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

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

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

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

         __lowerCamelCase		=  torch.cat(
             a  ,				dim=1  ,				)

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

         __lowerCamelCase		=  hidden_states + positional_embeddings

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

         if self.norm_in is not None:
             __lowerCamelCase		=  self.norm_in(a    )

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

         __lowerCamelCase		=  self.norm_out(a    )

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

         __lowerCamelCase		=  self.proj_to_clip_embeddings(a    )

         if not return_dict:
             return (predicted_image_embedding,)

         return PriorTransformerOutput(predicted_image_embedding=a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		int    ):





         """simple docstring"""



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



'''simple docstring'''






from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowerCAmelCase__											=	TypeVar('''KT''')
lowerCAmelCase__											=	TypeVar('''VT''')






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     KT | str = "root"					,lowercase__   :     VT | None = None			):
   __lowercase							  = key
   __lowercase							  = value
   __lowercase							  = []






  def __repr__(      self   :     Tuple			):
   return F"Node({self.key}: {self.value})"






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return len(self.forward			)






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     int					,lowercase__   :     float = 0.5					,lowercase__   :     int = 1_6			):
   __lowercase							  = Node[KT, VT]()
   __lowercase							  = 0
   __lowercase							  = p
   __lowercase							  = max_level






  def __str__(      self   :     List[str]			):
   __lowercase							  = list(self			)

   if len(lowercase__			) == 0:
    return F"SkipList(level={self.level})"

   __lowercase							  = max((len(str(lowercase__			)			) for item in items)					,default=4			)
   __lowercase							  = max(lowercase__					,4			) + 4

   __lowercase							  = self.head
   __lowercase							  = []

   __lowercase							  = node.forward.copy()
   lines.append(F"[{node.key}]".ljust(lowercase__					,'''-'''			) + '''* ''' * len(lowercase__			)			)
   lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)

   while len(node.forward			) != 0:
    __lowercase							  = node.forward[0]

    lines.append(
        F"[{node.key}]".ljust(lowercase__					,'''-'''			)
        + ''' '''.join(str(n.key			) if n.key == node.key else '''|''' for n in forwards			)			)
    lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)
    __lowercase							  = node.forward

   lines.append('''None'''.ljust(lowercase__			) + '''* ''' * len(lowercase__			)			)
   return F"SkipList(level={self.level})\n" + "\n".join(lowercase__			)






  def __iter__(      self   :     List[str]			):
   __lowercase							  = self.head

   while len(node.forward			) != 0:
    yield node.forward[0].key
    __lowercase							  = node.forward[0]






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = 1
   while random() < self.p and level < self.max_level:
    level += 1

   return level






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     str			):
   __lowercase							  = []

   __lowercase							  = self.head

   for i in reversed(range(self.level			)			):
    # i < node.level - When node level is lesser than `i` decrement `i`.
    # node.forward[i].key < key - Jumping to node with key value higher
    #                             or equal to searched key would result
    #                             in skipping searched key.
    while i < node.level and node.forward[i].key < key:
     __lowercase							  = node.forward[i]
    # Each leftmost node (relative to searched node) will potentially have to
    # be updated.
    update_vector.append(lowercase__			)

   update_vector.reverse()  # Note that we were inserting values in reverse order.

   # len(node.forward) != 0 - If current node doesn't contain any further
   #                          references then searched key is not present.
   # node.forward[0].key == key - Next node key should be equal to search key
   #                              if key is present.
   if len(node.forward			) != 0 and node.forward[0].key == key:
    return node.forward[0], update_vector
   else:
    return None, update_vector






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    for i, update_node in enumerate(lowercase__			):
     # Remove or replace all references to removed node.
     if update_node.level > i and update_node.forward[i].key == key:
      if node.level > i:
       __lowercase							  = node.forward[i]
      else:
       __lowercase							  = update_node.forward[:i]






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)
   if node is not None:
    __lowercase							  = value
   else:
    __lowercase							  = self.random_level()

    if level > self.level:
     # After level increase we have to add additional nodes to head.
     for _ in range(self.level - 1					,lowercase__			):
      update_vector.append(self.head			)
     __lowercase							  = level

    __lowercase							  = Node(lowercase__					,lowercase__			)

    for i, update_node in enumerate(update_vector[:level]			):
     # Change references to pass through new node.
     if update_node.level > i:
      new_node.forward.append(update_node.forward[i]			)

     if update_node.level < i + 1:
      update_node.forward.append(lowercase__			)
     else:
      __lowercase							  = new_node






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    return node.value

   return None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				3      )
 skip_list.insert('''Key2'''     ,				12      )
 skip_list.insert('''Key3'''     ,				41      )
 skip_list.insert('''Key4'''     ,				-19      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 assert len(A__      ) == 4
 assert all_values["Key1"] == 3
 assert all_values["Key2"] == 12
 assert all_values["Key3"] == 41
 assert all_values["Key4"] == -19


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				10      )
 skip_list.insert('''Key1'''     ,				12      )

 skip_list.insert('''Key5'''     ,				7      )
 skip_list.insert('''Key7'''     ,				10      )
 skip_list.insert('''Key10'''     ,				5      )

 skip_list.insert('''Key7'''     ,				7      )
 skip_list.insert('''Key5'''     ,				5      )
 skip_list.insert('''Key10'''     ,				10      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 if len(A__      ) != 4:
  print()
 assert len(A__      ) == 4
 assert all_values["Key1"] == 12
 assert all_values["Key7"] == 7
 assert all_values["Key5"] == 5
 assert all_values["Key10"] == 10


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 assert skip_list.find('''Some key'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key2'''     ,				20      )
 assert skip_list.find('''Key2'''      ) == 20

 skip_list.insert('''Some Key'''     ,				10      )
 skip_list.insert('''Key2'''     ,				8      )
 skip_list.insert('''V'''     ,				13      )

 assert skip_list.find('''Y'''      ) is None
 assert skip_list.find('''Key2'''      ) == 8
 assert skip_list.find('''Some Key'''      ) == 10
 assert skip_list.find('''V'''      ) == 13


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.delete('''Some key'''      )

 assert len(skip_list.head.forward      ) == 0


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 skip_list.delete('''Key2'''      )

 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) == 14
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''X'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key1'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key2'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				142      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''X'''      )

 def traverse_keys(A__      ):
  yield node.key
  for forward_node in node.forward:
   yield from traverse_keys(A__      )

 assert len(set(traverse_keys(skip_list.head      )      )      ) == 4


def 		_A     (    ):






 """simple docstring"""



 def is_sorted(A__      ):
  return all(next_item >= item for item, next_item in zip(A__     ,				lst[1:]      )      )

 __lowercase							  = SkipList()
 for i in range(10      ):
  skip_list.insert(A__     ,				A__      )
 assert is_sorted(list(A__      )      )
 skip_list.delete(5      )
 skip_list.delete(8      )
 skip_list.delete(2      )
 assert is_sorted(list(A__      )      )
 skip_list.insert(-12     ,				-12      )
 skip_list.insert(77     ,				77      )
 assert is_sorted(list(A__      )      )


def 		_A     (    ):






 """simple docstring"""



 for _ in range(100      ):
  # Repeat test 100 times due to the probabilistic nature of skip list
  # random values == random bugs
  test_insert()
  test_insert_overrides_existing_value()

  test_searching_empty_list_returns_none()
  test_search()

  test_deleting_item_from_empty_list_do_nothing()
  test_deleted_items_are_not_founded_by_find_method()
  test_delete_removes_only_given_key()
  test_delete_doesnt_leave_dead_nodes()

  test_iter_always_yields_sorted_values()


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert(2     ,				'''2'''      )
 skip_list.insert(4     ,				'''4'''      )
 skip_list.insert(6     ,				'''4'''      )
 skip_list.insert(4     ,				'''5'''      )
 skip_list.insert(8     ,				'''4'''      )
 skip_list.insert(9     ,				'''4'''      )

 skip_list.delete(4      )

 print(A__      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

