

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 (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      42





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




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

       SCREAMING_SNAKE_CASE										=   time_embed_dim or inner_dim
       SCREAMING_SNAKE_CASE										=   embedding_proj_dim or embedding_dim
       SCREAMING_SNAKE_CASE										=   clip_embed_dim or embedding_dim

       SCREAMING_SNAKE_CASE										=   Timesteps(__lowerCamelCase     , __lowerCamelCase     , 0			)
       SCREAMING_SNAKE_CASE										=   TimestepEmbedding(__lowerCamelCase     , __lowerCamelCase     , out_dim=__lowerCamelCase     , act_fn=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

       if embedding_proj_norm_type is None:
           SCREAMING_SNAKE_CASE										=   None
       elif embedding_proj_norm_type == "layer":
           SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)
       else:
           raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

       if encoder_hid_proj_type is None:
           SCREAMING_SNAKE_CASE										=   None
       elif encoder_hid_proj_type == "linear":
           SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)
       else:
           raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"			)

       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , num_embeddings + additional_embeddings     , __lowerCamelCase			)			)

       if added_emb_type == "prd":
           SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , 1     , __lowerCamelCase			)			)
       elif added_emb_type is None:
           SCREAMING_SNAKE_CASE										=   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										=   nn.ModuleList(
           [
               BasicTransformerBlock(
                   __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , dropout=__lowerCamelCase     , activation_fn="gelu"     , attention_bias=__lowerCamelCase     , )
               for d in range(__lowerCamelCase			)
           ]			)

       if norm_in_type == "layer":
           SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)
       elif norm_in_type is None:
           SCREAMING_SNAKE_CASE										=   None
       else:
           raise ValueError(f"Unsupported norm_in_type: {norm_in_type}."			)

       SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , __lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , __lowerCamelCase			)			)





   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   {}

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

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

           return processors

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

       return processors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[AttentionProcessor, Dict[str, AttentionProcessor]]			):
       SCREAMING_SNAKE_CASE										=   len(self.attn_processors.keys()			)

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

       def fn_recursive_attn_processor(__lowerCamelCase					:			str     , __lowerCamelCase					:			torch.nn.Module     , __lowerCamelCase					:			Union[str, Any]			):
           if hasattr(__lowerCamelCase     , "set_processor"			):
               if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
                   module.set_processor(__lowerCamelCase			)
               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}"     , __lowerCamelCase     , __lowerCamelCase			)

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





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





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

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

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

       SCREAMING_SNAKE_CASE										=   self.time_proj(__lowerCamelCase			)

       # 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										=   timesteps_projected.to(dtype=self.dtype			)
       SCREAMING_SNAKE_CASE										=   self.time_embedding(__lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   self.embedding_proj(__lowerCamelCase			)
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           SCREAMING_SNAKE_CASE										=   self.encoder_hidden_states_proj(__lowerCamelCase			)
       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										=   self.proj_in(__lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   0

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

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

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

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

       if self.prd_embedding is not None:
           SCREAMING_SNAKE_CASE										=   self.prd_embedding.to(hidden_states.dtype			).expand(__lowerCamelCase     , -1     , -1			)
           additional_embeds.append(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.cat(
           __lowerCamelCase     , dim=1     , )

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       SCREAMING_SNAKE_CASE										=   additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
           SCREAMING_SNAKE_CASE										=   F.pad(
               __lowerCamelCase     , (
                   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										=   hidden_states + positional_embeddings

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

       if self.norm_in is not None:
           SCREAMING_SNAKE_CASE										=   self.norm_in(__lowerCamelCase			)

       for block in self.transformer_blocks:
           SCREAMING_SNAKE_CASE										=   block(__lowerCamelCase     , attention_mask=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.norm_out(__lowerCamelCase			)

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

       SCREAMING_SNAKE_CASE										=   self.proj_to_clip_embeddings(__lowerCamelCase			)

       if not return_dict:
           return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=__lowerCamelCase			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents






'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



