
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]      ,				-10_000.0 )
        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 )) * -10_000.0
         __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








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






