
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      _UpperCAmelCase				(					lowercase__):
    __lowercase							: torch.FloatTensor






class      _UpperCAmelCase				(					lowercase__		,					lowercase__):
    @register_to_config
    def __init__(    self						,    snake_case_ = 32						,    snake_case_ = 64						,    snake_case_ = 20						,    snake_case_ = 7_68						,    snake_case_=77						,    snake_case_=4						,    snake_case_ = 0.0						,    snake_case_ = "silu"						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = "linear"						,    snake_case_ = "prd"						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = None						,    ):
      super().__init__()
      _snake_case     :						str						=   num_attention_heads
      _snake_case     :						Dict						=   attention_head_dim
      _snake_case     :						Tuple						=   num_attention_heads * attention_head_dim
      _snake_case     :						Dict						=   additional_embeddings

      _snake_case     :						List[str]						=   time_embed_dim or inner_dim
      _snake_case     :						str						=   embedding_proj_dim or embedding_dim
      _snake_case     :						Any						=   clip_embed_dim or embedding_dim

      _snake_case     :						Any						=   Timesteps(snake_case_						,    snake_case_						,    0     )
      _snake_case     :						int						=   TimestepEmbedding(snake_case_						,    snake_case_						,    out_dim=snake_case_						,    act_fn=snake_case_     )

      _snake_case     :						Optional[int]						=   nn.Linear(snake_case_						,    snake_case_     )

      if embedding_proj_norm_type is None:
        _snake_case     :						Union[str, Any]						=   None
      elif embedding_proj_norm_type == "layer":
        _snake_case     :						Any						=   nn.LayerNorm(snake_case_     )
      else:
        raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'     )

      _snake_case     :						Union[str, Any]						=   nn.Linear(snake_case_						,    snake_case_     )

      if encoder_hid_proj_type is None:
        _snake_case     :						Tuple						=   None
      elif encoder_hid_proj_type == "linear":
        _snake_case     :						List[str]						=   nn.Linear(snake_case_						,    snake_case_     )
      else:
        raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'     )

      _snake_case     :						Any						=   nn.Parameter(torch.zeros(1						,    num_embeddings + additional_embeddings						,    snake_case_     )     )

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

      _snake_case     :						Dict						=   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":
        _snake_case     :						int						=   nn.LayerNorm(snake_case_     )
      elif norm_in_type is None:
        _snake_case     :						int						=   None
      else:
        raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'     )

      _snake_case     :						Dict						=   nn.LayerNorm(snake_case_     )

      _snake_case     :						List[str]						=   nn.Linear(snake_case_						,    snake_case_     )

      _snake_case     :						Union[str, Any]						=   torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,    -10000.0     )
      causal_attention_mask.triu_(1     )
      _snake_case     :						Optional[Any]						=   causal_attention_mask[None, ...]
      self.register_buffer("causal_attention_mask"						,    snake_case_						,    persistent=snake_case_     )

      _snake_case     :						Union[str, Any]						=   nn.Parameter(torch.zeros(1						,    snake_case_     )     )
      _snake_case     :						Union[str, Any]						=   nn.Parameter(torch.zeros(1						,    snake_case_     )     )
    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   {}

      def fn_recursive_add_processors(snake_case_						,    snake_case_						,    snake_case_     ):
        if hasattr(snake_case_						,    "set_processor"     ):
          _snake_case     :						Optional[int]						=   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 					lowerCamelCase__	(    self						,    snake_case_     ):
      _snake_case     :						Union[str, Any]						=   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_						,    snake_case_						,    snake_case_     ):
        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 					lowerCamelCase__	(    self     ):
      self.set_attn_processor(AttnProcessor()     )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = True						,    ):
      _snake_case     :						str						=   hidden_states.shape[0]

      _snake_case     :						Tuple						=   timestep
      if not torch.is_tensor(snake_case_     ):
        _snake_case     :						Any						=   torch.tensor([timesteps]						,    dtype=torch.long						,    device=hidden_states.device     )
      elif torch.is_tensor(snake_case_     ) and len(timesteps.shape     ) == 0:
        _snake_case     :						List[Any]						=   timesteps[None].to(hidden_states.device     )

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

      _snake_case     :						List[str]						=   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.
      _snake_case     :						Union[str, Any]						=   timesteps_projected.to(dtype=self.dtype     )
      _snake_case     :						Dict						=   self.time_embedding(snake_case_     )

      if self.embedding_proj_norm is not None:
        _snake_case     :						List[str]						=   self.embedding_proj_norm(snake_case_     )

      _snake_case     :						Union[str, Any]						=   self.embedding_proj(snake_case_     )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
        _snake_case     :						Optional[Any]						=   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"     )

      _snake_case     :						Dict						=   self.proj_in(snake_case_     )

      _snake_case     :						str						=   self.positional_embedding.to(hidden_states.dtype     )

      _snake_case     :						Tuple						=   []
      _snake_case     :						Any						=   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:
        _snake_case     :						Any						=   proj_embeddings[:, None, :]

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

      _snake_case     :						Optional[int]						=   additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
        _snake_case     :						List[str]						=   self.prd_embedding.to(hidden_states.dtype     ).expand(snake_case_						,    -1						,    -1     )
        additional_embeds.append(snake_case_     )

      _snake_case     :						str						=   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
      _snake_case     :						int						=   additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
        _snake_case     :						Dict						=   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						,    )

      _snake_case     :						List[Any]						=   hidden_states + positional_embeddings

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

      if self.norm_in is not None:
        _snake_case     :						Tuple						=   self.norm_in(snake_case_     )

      for block in self.transformer_blocks:
        _snake_case     :						str						=   block(snake_case_						,    attention_mask=snake_case_     )

      _snake_case     :						List[Any]						=   self.norm_out(snake_case_     )

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

      _snake_case     :						Tuple						=   self.proj_to_clip_embeddings(snake_case_     )

      if not return_dict:
        return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      _snake_case     :						Union[str, Any]						=   (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents



"""simple docstring"""
from __future__ import annotations

from collections import namedtuple
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   namedtuple("result"       ,     "name value"		)
  if (voltage, current, power).count(0		) != 1:
    raise ValueError("Only one argument must be 0"		)
  elif power < 0:
    raise ValueError(
        "Power cannot be negative in any electrical/electronics system"		)
  elif voltage == 0:
    return result("voltage"       ,     power / current		)
  elif current == 0:
    return result("current"       ,     power / voltage		)
  elif power == 0:
    return result("power"       ,     float(round(abs(voltage * current		)       ,     2		)		)		)
  else:
    raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

