
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					):
							lowercase_		:						torch.FloatTensor

class       a		(   __UpperCAmelCase      ,	__UpperCAmelCase					):







							@register_to_config
							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  int = 32      ,				snake_case__  :  int = 64      ,				snake_case__  :  int = 20      ,				snake_case__  :  int = 768      ,				snake_case__  :  str=77      ,				snake_case__  :  Optional[int]=4      ,				snake_case__  :  float = 0.0      ,				snake_case__  :  str = "silu"      ,				snake_case__  :  Optional[str] = None      ,				snake_case__  :  Optional[str] = None      ,				snake_case__  :  Optional[str] = "linear"      ,				snake_case__  :  Optional[str] = "prd"      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  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(snake_case__      ,				snake_case__      ,				0 )
								__lowerCAmelCase         =							TimestepEmbedding(snake_case__      ,				snake_case__      ,				out_dim=snake_case__      ,				act_fn=snake_case__ )

								__lowerCAmelCase         =							nn.Linear(snake_case__      ,				snake_case__ )

								if embedding_proj_norm_type is None:
									__lowerCAmelCase         =							None
								elif embedding_proj_norm_type == "layer":
									__lowerCAmelCase         =							nn.LayerNorm(snake_case__ )
								else:
									raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}" )

								__lowerCAmelCase         =							nn.Linear(snake_case__      ,				snake_case__ )

								if encoder_hid_proj_type is None:
									__lowerCAmelCase         =							None
								elif encoder_hid_proj_type == "linear":
									__lowerCAmelCase         =							nn.Linear(snake_case__      ,				snake_case__ )
								else:
									raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}" )

								__lowerCAmelCase         =							nn.Parameter(torch.zeros(1      ,				num_embeddings + additional_embeddings      ,				snake_case__ ) )

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

								if norm_in_type == "layer":
									__lowerCAmelCase         =							nn.LayerNorm(snake_case__ )
								elif norm_in_type is None:
									__lowerCAmelCase         =							None
								else:
									raise ValueError(F"Unsupported norm_in_type: {norm_in_type}." )

								__lowerCAmelCase         =							nn.LayerNorm(snake_case__ )

								__lowerCAmelCase         =							nn.Linear(snake_case__      ,				snake_case__ )

								__lowerCAmelCase         =							torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      ,				-1_0_0_0_0.0 )
								causal_attention_mask.triu_(1 )
								__lowerCAmelCase         =							causal_attention_mask[None, ...]
								self.register_buffer("causal_attention_mask"      ,				snake_case__      ,				persistent=snake_case__ )

								__lowerCAmelCase         =							nn.Parameter(torch.zeros(1      ,				snake_case__ ) )
								__lowerCAmelCase         =							nn.Parameter(torch.zeros(1      ,				snake_case__ ) )







							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							{}

								def fn_recursive_add_processors(snake_case__  :  str      ,				snake_case__  :  torch.nn.Module      ,				snake_case__  :  Dict[str, AttentionProcessor] ):
									if hasattr(snake_case__      ,				"set_processor" ):
										__lowerCAmelCase         =							module.processor

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

									return processors

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

								return processors







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
								"""simple docstring"""







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

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

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

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







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								self.set_attn_processor(AttnProcessor() )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Union[torch.Tensor, float, int]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  Optional[torch.FloatTensor] = None      ,				snake_case__  :  Optional[torch.BoolTensor] = None      ,				snake_case__  :  bool = True      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							hidden_states.shape[0]

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

								__lowerCAmelCase         =							self.time_proj(snake_case__ )

								# 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(snake_case__ )

								if self.embedding_proj_norm is not None:
									__lowerCAmelCase         =							self.embedding_proj_norm(snake_case__ )

								__lowerCAmelCase         =							self.embedding_proj(snake_case__ )
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
									__lowerCAmelCase         =							self.encoder_hidden_states_proj(snake_case__ )
								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(snake_case__ )

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

								__lowerCAmelCase         =							[]
								__lowerCAmelCase         =							0

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

								__lowerCAmelCase         =							torch.cat(
								    snake_case__      ,				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(
									    snake_case__      ,				(
									        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_0_0_0_0.0
									__lowerCAmelCase         =							F.pad(snake_case__      ,				(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(snake_case__ )

								for block in self.transformer_blocks:
									__lowerCAmelCase         =							block(snake_case__      ,				attention_mask=snake_case__ )

								__lowerCAmelCase         =							self.norm_out(snake_case__ )

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

								__lowerCAmelCase         =							self.proj_to_clip_embeddings(snake_case__ )

								if not return_dict:
									return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







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








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							data
								__lowerCAmelCase         =							None

class       a		:







							def __init__(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							None







							def __iter__(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.head
								while self.head:
									yield node.data
									__lowerCAmelCase         =							node.next
									if node == self.head:
										break







							def __len__(						self  :  Dict ):
								"""simple docstring"""







								return sum(1 for _ in self )







							def __repr__(						self  :  str ):
								"""simple docstring"""







								return "->".join(str(snake_case__ ) for item in iter(self ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(len(self )      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(0      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Any ):
								"""simple docstring"""







								if index < 0 or index > len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							Node(snake_case__ )
								if self.head is None:
									__lowerCAmelCase         =							new_node  # first node points itself
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								elif index == 0:  # insert at head
									__lowerCAmelCase         =							self.head
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							new_node
									if index == len(self ) - 1:  # insert at tail
										__lowerCAmelCase         =							new_node







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								return self.delete_nth(0 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.delete_nth(len(self ) - 1 )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int = 0 ):
								"""simple docstring"""







								if not 0 <= index < len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							self.head
								if self.head == self.tail:  # just one node
									__lowerCAmelCase         =							__lowerCAmelCase         =							None
								elif index == 0:  # delete head node
									__lowerCAmelCase         =							self.tail.next.next
									__lowerCAmelCase         =							self.head.next
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next.next
									if index == len(self ) - 1:  # delete at tail
										__lowerCAmelCase         =							temp
								return delete_node.data







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return len(self ) == 0






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase			,							i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0			,							7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2			,							3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()






