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 		_lowercase						(					snake_case_       ):
 lowercase				     =   42





class 		_lowercase						(					snake_case_						,      snake_case_       ):

 @register_to_config
 def __init__( self	:							Union[str, Any]   ,      snake_case	:							int = 3_2   ,      snake_case	:							int = 6_4   ,      snake_case	:							int = 2_0   ,      snake_case	:							int = 7_6_8   ,      snake_case	:							Union[str, Any]=7_7   ,      snake_case	:							List[str]=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   ,      )   ->						Optional[Any]:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						Tuple =					num_attention_heads
  UpperCamelCase_					:						List[str] =					attention_head_dim
  UpperCamelCase_					:						Optional[int] =					num_attention_heads * attention_head_dim
  UpperCamelCase_					:						int =					additional_embeddings

  UpperCamelCase_					:						int =					time_embed_dim or inner_dim
  UpperCamelCase_					:						Any =					embedding_proj_dim or embedding_dim
  UpperCamelCase_					:						Optional[int] =					clip_embed_dim or embedding_dim

  UpperCamelCase_					:						int =					Timesteps(snake_case   ,      snake_case   ,      0			)
  UpperCamelCase_					:						Tuple =					TimestepEmbedding(snake_case   ,      snake_case   ,      out_dim=snake_case   ,      act_fn=snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					nn.Linear(snake_case   ,      snake_case			)

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

  UpperCamelCase_					:						List[str] =					nn.Linear(snake_case   ,      snake_case			)

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

  UpperCamelCase_					:						List[str] =					nn.Parameter(torch.zeros(1   ,      num_embeddings + additional_embeddings   ,      snake_case			)			)

  if added_emb_type == "prd":
   UpperCamelCase_					:						List[str] =					nn.Parameter(torch.zeros(1   ,      1   ,      snake_case			)			)
  elif added_emb_type is None:
   UpperCamelCase_					:						Dict =					None
  else:
   raise ValueError(
       f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."			)

  UpperCamelCase_					:						str =					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":
   UpperCamelCase_					:						str =					nn.LayerNorm(snake_case			)
  elif norm_in_type is None:
   UpperCamelCase_					:						Any =					None
  else:
   raise ValueError(f"Unsupported norm_in_type: {norm_in_type}."			)

  UpperCamelCase_					:						Union[str, Any] =					nn.LayerNorm(snake_case			)

  UpperCamelCase_					:						List[str] =					nn.Linear(snake_case   ,      snake_case			)

  UpperCamelCase_					:						Optional[Any] =					torch.full(
      [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]   ,      -10000.0			)
  causal_attention_mask.triu_(1			)
  UpperCamelCase_					:						Optional[int] =					causal_attention_mask[None, ...]
  self.register_buffer('causal_attention_mask'   ,      snake_case   ,      persistent=snake_case			)

  UpperCamelCase_					:						str =					nn.Parameter(torch.zeros(1   ,      snake_case			)			)
  UpperCamelCase_					:						List[Any] =					nn.Parameter(torch.zeros(1   ,      snake_case			)			)

 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Dict[str, AttentionProcessor]:

  """simple docstring"""





  UpperCamelCase_					:						int =					{}

  def fn_recursive_add_processors(snake_case	:							str   ,      snake_case	:							torch.nn.Module   ,      snake_case	:							Dict[str, AttentionProcessor]			):
   if hasattr(snake_case   ,      'set_processor'			):
    UpperCamelCase_					:						List[str] =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Union[AttentionProcessor, Dict[str, AttentionProcessor]]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					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	:							Optional[Any]			):
   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 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						int:

  """simple docstring"""





  self.set_attn_processor(AttnProcessor()			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      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   ,      )   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					hidden_states.shape[0]

  UpperCamelCase_					:						List[Any] =					timestep
  if not torch.is_tensor(snake_case			):
   UpperCamelCase_					:						int =					torch.tensor([timesteps]   ,      dtype=torch.long   ,      device=hidden_states.device			)
  elif torch.is_tensor(snake_case			) and len(timesteps.shape			) == 0:
   UpperCamelCase_					:						Union[str, Any] =					timesteps[None].to(hidden_states.device			)

  # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  UpperCamelCase_					:						Union[str, Any] =					timesteps * torch.ones(snake_case   ,      dtype=timesteps.dtype   ,      device=timesteps.device			)

  UpperCamelCase_					:						Optional[Any] =					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.
  UpperCamelCase_					:						Optional[int] =					timesteps_projected.to(dtype=self.dtype			)
  UpperCamelCase_					:						Tuple =					self.time_embedding(snake_case			)

  if self.embedding_proj_norm is not None:
   UpperCamelCase_					:						List[Any] =					self.embedding_proj_norm(snake_case			)

  UpperCamelCase_					:						Dict =					self.embedding_proj(snake_case			)
  if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
   UpperCamelCase_					:						str =					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'			)

  UpperCamelCase_					:						Any =					self.proj_in(snake_case			)

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

  UpperCamelCase_					:						Union[str, Any] =					[]
  UpperCamelCase_					:						Tuple =					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:
   UpperCamelCase_					:						int =					proj_embeddings[:, None, :]

  if len(hidden_states.shape			) == 2:
   UpperCamelCase_					:						Optional[int] =					hidden_states[:, None, :]

  UpperCamelCase_					:						Dict =					additional_embeds + [
      proj_embeddings,
      time_embeddings[:, None, :],
      hidden_states,
  ]

  if self.prd_embedding is not None:
   UpperCamelCase_					:						Optional[Any] =					self.prd_embedding.to(hidden_states.dtype			).expand(snake_case   ,      -1   ,      -1			)
   additional_embeds.append(snake_case			)

  UpperCamelCase_					:						Optional[int] =					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
  UpperCamelCase_					:						int =					additional_embeddings_len + proj_embeddings.shape[1] + 1
  if positional_embeddings.shape[1] < hidden_states.shape[1]:
   UpperCamelCase_					:						Union[str, Any] =					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   ,      )

  UpperCamelCase_					:						Optional[Any] =					hidden_states + positional_embeddings

  if attention_mask is not None:
   UpperCamelCase_					:						Tuple =					(1 - attention_mask.to(hidden_states.dtype			)) * -10000.0
   UpperCamelCase_					:						Union[str, Any] =					F.pad(snake_case   ,      (0, self.additional_embeddings)   ,      value=0.0			)
   UpperCamelCase_					:						List[str] =					(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype			)
   UpperCamelCase_					:						Dict =					attention_mask.repeat_interleave(self.config.num_attention_heads   ,      dim=0			)

  if self.norm_in is not None:
   UpperCamelCase_					:						List[Any] =					self.norm_in(snake_case			)

  for block in self.transformer_blocks:
   UpperCamelCase_					:						List[Any] =					block(snake_case   ,      attention_mask=snake_case			)

  UpperCamelCase_					:						Tuple =					self.norm_out(snake_case			)

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

  UpperCamelCase_					:						Any =					self.proj_to_clip_embeddings(snake_case			)

  if not return_dict:
   return (predicted_image_embedding,)

  return PriorTransformerOutput(predicted_image_embedding=snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							int			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					(prior_latents * self.clip_std) + self.clip_mean
  return prior_latents




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



