






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     @register_to_config
     def __init__(				self       :	Tuple					, UpperCAmelCase_       :	int = 32					, UpperCAmelCase_       :	int = 64					, UpperCAmelCase_       :	int = 20					, UpperCAmelCase_       :	int = 768					, UpperCAmelCase_       :	Optional[Any]=77					, UpperCAmelCase_       :	Tuple=4					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	str = "silu"					, UpperCAmelCase_       :	Optional[str] = None					, UpperCAmelCase_       :	Optional[str] = None					, UpperCAmelCase_       :	Optional[str] = "linear"					, UpperCAmelCase_       :	Optional[str] = "prd"					, UpperCAmelCase_       :	Optional[int] = None					, UpperCAmelCase_       :	Optional[int] = None					, UpperCAmelCase_       :	Optional[int] = None					, ):
       super().__init__()
       SCREAMING_SNAKE_CASE       :      Any							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    attention_head_dim
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_attention_heads * attention_head_dim
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    additional_embeddings

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

       SCREAMING_SNAKE_CASE       :      int							=    Timesteps(UpperCAmelCase_					, UpperCAmelCase_					, 0      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    TimestepEmbedding(UpperCAmelCase_					, UpperCAmelCase_					, out_dim=UpperCAmelCase_					, act_fn=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    nn.Linear(UpperCAmelCase_					, UpperCAmelCase_      )

       if embedding_proj_norm_type is None:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    None
       elif embedding_proj_norm_type == "layer":
         SCREAMING_SNAKE_CASE       :      Any							=    nn.LayerNorm(UpperCAmelCase_      )
       else:
         raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''      )

       SCREAMING_SNAKE_CASE       :      str							=    nn.Linear(UpperCAmelCase_					, UpperCAmelCase_      )

       if encoder_hid_proj_type is None:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    None
       elif encoder_hid_proj_type == "linear":
         SCREAMING_SNAKE_CASE       :      Tuple							=    nn.Linear(UpperCAmelCase_					, UpperCAmelCase_      )
       else:
         raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    nn.Parameter(torch.zeros(1					, num_embeddings + additional_embeddings					, UpperCAmelCase_      )      )

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

       SCREAMING_SNAKE_CASE       :      Dict							=    nn.ModuleList(
           [
               BasicTransformerBlock(
                   UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dropout=UpperCAmelCase_					, activation_fn="gelu"					, attention_bias=UpperCAmelCase_					, )
               for d in range(UpperCAmelCase_      )
           ]      )

       if norm_in_type == "layer":
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.LayerNorm(UpperCAmelCase_      )
       elif norm_in_type is None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    None
       else:
         raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    nn.LayerNorm(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Dict							=    nn.Linear(UpperCAmelCase_					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]					, -10_000.0      )
       causal_attention_mask.triu_(1      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    causal_attention_mask[None, ...]
       self.register_buffer("causal_attention_mask"					, UpperCAmelCase_					, persistent=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Dict							=    nn.Parameter(torch.zeros(1					, UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.Parameter(torch.zeros(1					, UpperCAmelCase_      )      )


     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {}

       def fn_recursive_add_processors(UpperCAmelCase_       :	str					, UpperCAmelCase_       :	torch.nn.Module					, UpperCAmelCase_       :	Dict[str, AttentionProcessor]      ):
         if hasattr(UpperCAmelCase_					, "set_processor"      ):
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    module.processor

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

         return processors

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

       return processors


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Union[AttentionProcessor, Dict[str, AttentionProcessor]]      ):
       SCREAMING_SNAKE_CASE       :      str							=    len(self.attn_processors.keys()      )

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

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

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


     def 				_A       (				self       :	Optional[int]      ):
       self.set_attn_processor(AttnProcessor()      )


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[torch.Tensor, float, int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	Optional[torch.BoolTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      int							=    hidden_states.shape[0]

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

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

       SCREAMING_SNAKE_CASE       :      Dict							=    self.time_proj(UpperCAmelCase_      )

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

       if self.embedding_proj_norm is not None:
         SCREAMING_SNAKE_CASE       :      int							=    self.embedding_proj_norm(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.embedding_proj(UpperCAmelCase_      )
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
         SCREAMING_SNAKE_CASE       :      List[str]							=    self.encoder_hidden_states_proj(UpperCAmelCase_      )
       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"      )

       SCREAMING_SNAKE_CASE       :      Dict							=    self.proj_in(UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    0

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

       if len(proj_embeddings.shape      ) == 2:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    proj_embeddings[:, None, :]

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

       SCREAMING_SNAKE_CASE       :      Tuple							=    additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

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

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.cat(
           UpperCAmelCase_					, dim=1					, )

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    hidden_states + positional_embeddings

       if attention_mask is not None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
         SCREAMING_SNAKE_CASE       :      Dict							=    F.pad(UpperCAmelCase_					, (0, self.additional_embeddings)					, value=0.0      )
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    attention_mask.repeat_interleave(self.config.num_attention_heads					, dim=0      )

       if self.norm_in is not None:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.norm_in(UpperCAmelCase_      )

       for block in self.transformer_blocks:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    block(UpperCAmelCase_					, attention_mask=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.norm_out(UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.proj_to_clip_embeddings(UpperCAmelCase_      )

       if not return_dict:
         return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=UpperCAmelCase_      )






     def 				_A       (				self       :	int					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents














from __future__ import annotations

from cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






