


'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      :str			    =       logging.get_logger(__name__)




def      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          __UpperCAmelCase     :			Dict  							=      [144, 192, 240]
          __UpperCAmelCase     :			Union[str, Any]  							=      [16, 32, 64, 96, 128, 160, 640]
     elif "mobilevit_xs" in mobilevit_name:
          __UpperCAmelCase     :			str  							=      [96, 120, 144]
          __UpperCAmelCase     :			List[str]  							=      [16, 32, 48, 64, 80, 96, 384]
     elif "mobilevit_xxs" in mobilevit_name:
          __UpperCAmelCase     :			List[Any]  							=      [64, 80, 96]
          __UpperCAmelCase     :			int  							=      [16, 16, 24, 48, 64, 80, 320]
          __UpperCAmelCase     :			str  							=      0.05
          __UpperCAmelCase     :			Dict  							=      2.0

     if mobilevit_name.startswith('''deeplabv3_'''  ):
          __UpperCAmelCase     :			List[str]  							=      512
          __UpperCAmelCase     :			str  							=      16
          __UpperCAmelCase     :			str  							=      21
          __UpperCAmelCase     :			Optional[Any]  							=      '''pascal-voc-id2label.json'''
     else:
          __UpperCAmelCase     :			Any  							=      1000
          __UpperCAmelCase     :			Dict  							=      '''imagenet-1k-id2label.json'''

     __UpperCAmelCase     :			List[Any]  							=      '''huggingface/label-files'''
     __UpperCAmelCase     :			Union[str, Any]  							=      json.load(open(hf_hub_download(_lowercase      , _lowercase      , repo_type='''dataset'''  )      , '''r'''  )  )
     __UpperCAmelCase     :			int  							=      {int(_lowercase  ): v for k, v in idalabel.items()}
     __UpperCAmelCase     :			Optional[int]  							=      idalabel
     __UpperCAmelCase     :			Dict  							=      {v: k for k, v in idalabel.items()}

     return config




def      _a     (						_lowercase :     Dict      , _lowercase :     Optional[Any]=False  ):





     '''simple docstring'''






     for i in range(1      , 6  ):
          if F'layer_{i}.' in name:
               __UpperCAmelCase     :			Any  							=      name.replace(F'layer_{i}.'      , F'encoder.layer.{i - 1}.'  )

     if "conv_1." in name:
          __UpperCAmelCase     :			int  							=      name.replace('''conv_1.'''      , '''conv_stem.'''  )
     if ".block." in name:
          __UpperCAmelCase     :			Union[str, Any]  							=      name.replace('''.block.'''      , '''.'''  )

     if "exp_1x1" in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''exp_1x1'''      , '''expand_1x1'''  )
     if "red_1x1" in name:
          __UpperCAmelCase     :			Optional[int]  							=      name.replace('''red_1x1'''      , '''reduce_1x1'''  )
     if ".local_rep.conv_3x3." in name:
          __UpperCAmelCase     :			List[str]  							=      name.replace('''.local_rep.conv_3x3.'''      , '''.conv_kxk.'''  )
     if ".local_rep.conv_1x1." in name:
          __UpperCAmelCase     :			int  							=      name.replace('''.local_rep.conv_1x1.'''      , '''.conv_1x1.'''  )
     if ".norm." in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''.norm.'''      , '''.normalization.'''  )
     if ".conv." in name:
          __UpperCAmelCase     :			int  							=      name.replace('''.conv.'''      , '''.convolution.'''  )
     if ".conv_proj." in name:
          __UpperCAmelCase     :			List[str]  							=      name.replace('''.conv_proj.'''      , '''.conv_projection.'''  )

     for i in range(0      , 2  ):
          for j in range(0      , 4  ):
               if F'.{i}.{j}.' in name:
                    __UpperCAmelCase     :			Tuple  							=      name.replace(F'.{i}.{j}.'      , F'.{i}.layer.{j}.'  )

     for i in range(2      , 6  ):
          for j in range(0      , 4  ):
               if F'.{i}.{j}.' in name:
                    __UpperCAmelCase     :			int  							=      name.replace(F'.{i}.{j}.'      , F'.{i}.'  )
                    if "expand_1x1" in name:
                         __UpperCAmelCase     :			int  							=      name.replace('''expand_1x1'''      , '''downsampling_layer.expand_1x1'''  )
                    if "conv_3x3" in name:
                         __UpperCAmelCase     :			Dict  							=      name.replace('''conv_3x3'''      , '''downsampling_layer.conv_3x3'''  )
                    if "reduce_1x1" in name:
                         __UpperCAmelCase     :			Tuple  							=      name.replace('''reduce_1x1'''      , '''downsampling_layer.reduce_1x1'''  )

     for i in range(2      , 5  ):
          if F'.global_rep.{i}.weight' in name:
               __UpperCAmelCase     :			Optional[int]  							=      name.replace(F'.global_rep.{i}.weight'      , '''.layernorm.weight'''  )
          if F'.global_rep.{i}.bias' in name:
               __UpperCAmelCase     :			List[str]  							=      name.replace(F'.global_rep.{i}.bias'      , '''.layernorm.bias'''  )

     if ".global_rep." in name:
          __UpperCAmelCase     :			Optional[int]  							=      name.replace('''.global_rep.'''      , '''.transformer.'''  )
     if ".pre_norm_mha.0." in name:
          __UpperCAmelCase     :			Optional[int]  							=      name.replace('''.pre_norm_mha.0.'''      , '''.layernorm_before.'''  )
     if ".pre_norm_mha.1.out_proj." in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''.pre_norm_mha.1.out_proj.'''      , '''.attention.output.dense.'''  )
     if ".pre_norm_ffn.0." in name:
          __UpperCAmelCase     :			str  							=      name.replace('''.pre_norm_ffn.0.'''      , '''.layernorm_after.'''  )
     if ".pre_norm_ffn.1." in name:
          __UpperCAmelCase     :			Tuple  							=      name.replace('''.pre_norm_ffn.1.'''      , '''.intermediate.dense.'''  )
     if ".pre_norm_ffn.4." in name:
          __UpperCAmelCase     :			Optional[int]  							=      name.replace('''.pre_norm_ffn.4.'''      , '''.output.dense.'''  )
     if ".transformer." in name:
          __UpperCAmelCase     :			Any  							=      name.replace('''.transformer.'''      , '''.transformer.layer.'''  )

     if ".aspp_layer." in name:
          __UpperCAmelCase     :			Any  							=      name.replace('''.aspp_layer.'''      , '''.'''  )
     if ".aspp_pool." in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''.aspp_pool.'''      , '''.'''  )
     if "seg_head." in name:
          __UpperCAmelCase     :			Dict  							=      name.replace('''seg_head.'''      , '''segmentation_head.'''  )
     if "segmentation_head.classifier.classifier." in name:
          __UpperCAmelCase     :			Any  							=      name.replace('''segmentation_head.classifier.classifier.'''      , '''segmentation_head.classifier.'''  )

     if "classifier.fc." in name:
          __UpperCAmelCase     :			int  							=      name.replace('''classifier.fc.'''      , '''classifier.'''  )
     elif (not base_model) and ("segmentation_head." not in name):
          __UpperCAmelCase     :			int  							=      '''mobilevit.''' + name

     return name




def      _a     (						_lowercase :     Any      , _lowercase :     str      , _lowercase :     Union[str, Any]=False  ):





     '''simple docstring'''






     if base_model:
          __UpperCAmelCase     :			List[str]  							=      ''''''
     else:
          __UpperCAmelCase     :			int  							=      '''mobilevit.'''

     for key in orig_state_dict.copy().keys():
          __UpperCAmelCase     :			int  							=      orig_state_dict.pop(_lowercase  )

          if key[:8] == "encoder.":
               __UpperCAmelCase     :			Union[str, Any]  							=      key[8:]

          if "qkv" in key:
               __UpperCAmelCase     :			Dict  							=      key.split('''.'''  )
               __UpperCAmelCase     :			Optional[int]  							=      int(key_split[0][6:]  ) - 1
               __UpperCAmelCase     :			int  							=      int(key_split[3]  )
               __UpperCAmelCase     :			Optional[Any]  							=      model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'  )
               __UpperCAmelCase     :			str  							=      layer.transformer.layer[transformer_num].attention.attention.all_head_size
               __UpperCAmelCase     :			Optional[int]  							=      (
                   F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
               )
               if "weight" in key:
                    __UpperCAmelCase     :			Union[str, Any]  							=      val[:dim, :]
                    __UpperCAmelCase     :			Optional[Any]  							=      val[dim : dim * 2, :]
                    __UpperCAmelCase     :			List[str]  							=      val[-dim:, :]
               else:
                    __UpperCAmelCase     :			Tuple  							=      val[:dim]
                    __UpperCAmelCase     :			Optional[Any]  							=      val[dim : dim * 2]
                    __UpperCAmelCase     :			str  							=      val[-dim:]
          else:
               __UpperCAmelCase     :			List[str]  							=      val

     return orig_state_dict




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase     :			Union[str, Any]  							=      Image.open(requests.get(_lowercase      , stream=_lowercase  ).raw  )
     return im




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Any      , _lowercase :     Optional[int]      , _lowercase :     Union[str, Any]=False  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      get_mobilevit_config(_lowercase  )

     # load original state_dict
     __UpperCAmelCase     :			List[Any]  							=      torch.load(_lowercase      , map_location='''cpu'''  )

     # load 🤗 model
     if mobilevit_name.startswith('''deeplabv3_'''  ):
          __UpperCAmelCase     :			List[str]  							=      MobileViTForSemanticSegmentation(_lowercase  ).eval()
     else:
          __UpperCAmelCase     :			Dict  							=      MobileViTForImageClassification(_lowercase  ).eval()

     __UpperCAmelCase     :			int  							=      convert_state_dict(_lowercase      , _lowercase  )
     model.load_state_dict(_lowercase  )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     __UpperCAmelCase     :			Dict  							=      MobileViTImageProcessor(crop_size=config.image_size      , size=config.image_size + 32  )
     __UpperCAmelCase     :			List[str]  							=      image_processor(images=prepare_img()      , return_tensors='''pt'''  )
     __UpperCAmelCase     :			List[str]  							=      model(**_lowercase  )
     __UpperCAmelCase     :			str  							=      outputs.logits

     if mobilevit_name.startswith('''deeplabv3_'''  ):
          assert logits.shape == (1, 21, 32, 32)

          if mobilevit_name == "deeplabv3_mobilevit_s":
               __UpperCAmelCase     :			List[Any]  							=      torch.tensor(
                   [
                       [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                       [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                       [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                   ]  )
          elif mobilevit_name == "deeplabv3_mobilevit_xs":
               __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor(
                   [
                       [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                       [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                       [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                   ]  )
          elif mobilevit_name == "deeplabv3_mobilevit_xxs":
               __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor(
                   [
                       [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                       [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                       [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                   ]  )
          else:
               raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

          assert torch.allclose(logits[0, :3, :3, :3]      , _lowercase      , atol=1E-4  )
     else:
          assert logits.shape == (1, 1000)

          if mobilevit_name == "mobilevit_s":
               __UpperCAmelCase     :			str  							=      torch.tensor([-0.9866, 0.2392, -1.1241]  )
          elif mobilevit_name == "mobilevit_xs":
               __UpperCAmelCase     :			Any  							=      torch.tensor([-2.4761, -0.9399, -1.9587]  )
          elif mobilevit_name == "mobilevit_xxs":
               __UpperCAmelCase     :			Optional[Any]  							=      torch.tensor([-1.9364, -1.2327, -0.4653]  )
          else:
               raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

          assert torch.allclose(logits[0, :3]      , _lowercase      , atol=1E-4  )

     Path(_lowercase  ).mkdir(exist_ok=_lowercase  )
     print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'  )
     model.save_pretrained(_lowercase  )
     print(F'Saving image processor to {pytorch_dump_folder_path}'  )
     image_processor.save_pretrained(_lowercase  )

     if push_to_hub:
          __UpperCAmelCase     :			Optional[int]  							=      {
              '''mobilevit_s''': '''mobilevit-small''',
              '''mobilevit_xs''': '''mobilevit-x-small''',
              '''mobilevit_xxs''': '''mobilevit-xx-small''',
              '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
              '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
              '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
          }

          print('''Pushing to the hub...'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      model_mapping[mobilevit_name]
          image_processor.push_to_hub(_lowercase      , organization='''apple'''  )
          model.push_to_hub(_lowercase      , organization='''apple'''  )


if __name__ == "__main__":
      __UpperCAmelCase      :int			    =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--mobilevit_name",
          default="mobilevit_s",
          type=str,
          help=(
              "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
              " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
          ),
      )
      parser.add_argument(
          "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )

      __UpperCAmelCase      :str			    =       parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )



'''simple docstring'''



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    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.FloatTensor



class      a    (		_a     ,  _a	):


     """simple docstring"""






     @register_to_config
     def __init__(    self					:		str							,			snake_case					:		int = 32							,			snake_case					:		int = 64							,			snake_case					:		int = 20							,			snake_case					:		int = 768							,			snake_case					:		Tuple=77							,			snake_case					:		List[Any]=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							,			)						->							List[str]:
          super().__init__()
          __UpperCAmelCase     :			List[Any]  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      attention_head_dim
          __UpperCAmelCase     :			int  							=      num_attention_heads * attention_head_dim
          __UpperCAmelCase     :			List[Any]  							=      additional_embeddings

          __UpperCAmelCase     :			Any  							=      time_embed_dim or inner_dim
          __UpperCAmelCase     :			Any  							=      embedding_proj_dim or embedding_dim
          __UpperCAmelCase     :			Union[str, Any]  							=      clip_embed_dim or embedding_dim

          __UpperCAmelCase     :			List[Any]  							=      Timesteps(snake_case							,			snake_case							,			0      )
          __UpperCAmelCase     :			Optional[Any]  							=      TimestepEmbedding(snake_case							,			snake_case							,			out_dim=snake_case							,			act_fn=snake_case      )

          __UpperCAmelCase     :			str  							=      nn.Linear(snake_case							,			snake_case      )

          if embedding_proj_norm_type is None:
               __UpperCAmelCase     :			str  							=      None
          elif embedding_proj_norm_type == "layer":
               __UpperCAmelCase     :			str  							=      nn.LayerNorm(snake_case      )
          else:
               raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'      )

          __UpperCAmelCase     :			List[Any]  							=      nn.Linear(snake_case							,			snake_case      )

          if encoder_hid_proj_type is None:
               __UpperCAmelCase     :			Union[str, Any]  							=      None
          elif encoder_hid_proj_type == "linear":
               __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )
          else:
               raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'      )

          __UpperCAmelCase     :			Dict  							=      nn.Parameter(torch.zeros(1							,			num_embeddings + additional_embeddings							,			snake_case      )      )

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

          __UpperCAmelCase     :			Optional[int]  							=      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     :			Tuple  							=      nn.LayerNorm(snake_case      )
          elif norm_in_type is None:
               __UpperCAmelCase     :			List[Any]  							=      None
          else:
               raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'      )

          __UpperCAmelCase     :			Dict  							=      nn.LayerNorm(snake_case      )

          __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )

          __UpperCAmelCase     :			Any  							=      torch.full(
              [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,			-10_000.0      )
          causal_attention_mask.triu_(1      )
          __UpperCAmelCase     :			str  							=      causal_attention_mask[None, ...]
          self.register_buffer('''causal_attention_mask'''							,			snake_case							,			persistent=snake_case      )

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

     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 			lowerCamelCase__			(    self					:		int      )						->							Dict[str, AttentionProcessor]:
          __UpperCAmelCase     :			Optional[Any]  							=      {}

          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     :			Union[str, Any]  							=      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					:		Optional[Any]							,			snake_case					:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			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					:		str							,			snake_case					:		torch.nn.Module							,			snake_case					:		int      ):
               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					:		str      )						->							Tuple:
          self.set_attn_processor(AttnProcessor()      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[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							,			)						->							List[Any]:
          __UpperCAmelCase     :			Any  							=      hidden_states.shape[0]

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

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

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

          if self.embedding_proj_norm is not None:
               __UpperCAmelCase     :			Optional[Any]  							=      self.embedding_proj_norm(snake_case      )

          __UpperCAmelCase     :			str  							=      self.embedding_proj(snake_case      )
          if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               __UpperCAmelCase     :			Dict  							=      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     :			Optional[int]  							=      self.proj_in(snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.positional_embedding.to(hidden_states.dtype      )

          __UpperCAmelCase     :			Union[str, Any]  							=      []
          __UpperCAmelCase     :			Optional[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:
               __UpperCAmelCase     :			Optional[int]  							=      proj_embeddings[:, None, :]

          if len(hidden_states.shape      ) == 2:
               __UpperCAmelCase     :			Union[str, Any]  							=      hidden_states[:, None, :]

          __UpperCAmelCase     :			Union[str, Any]  							=      additional_embeds + [
              proj_embeddings,
              time_embeddings[:, None, :],
              hidden_states,
          ]

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

          __UpperCAmelCase     :			Dict  							=      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     :			str  							=      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[int]  							=      hidden_states + positional_embeddings

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

          if self.norm_in is not None:
               __UpperCAmelCase     :			str  							=      self.norm_in(snake_case      )

          for block in self.transformer_blocks:
               __UpperCAmelCase     :			Optional[int]  							=      block(snake_case							,			attention_mask=snake_case      )

          __UpperCAmelCase     :			int  							=      self.norm_out(snake_case      )

          if self.prd_embedding is not None:
               __UpperCAmelCase     :			Optional[int]  							=      hidden_states[:, -1]
          else:
               __UpperCAmelCase     :			List[Any]  							=      hidden_states[:, additional_embeddings_len:]

          __UpperCAmelCase     :			Dict  							=      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					:		Optional[int]							,			snake_case					:		List[str]      )						->							str:
          __UpperCAmelCase     :			Dict  							=      (prior_latents * self.clip_std) + self.clip_mean
          return prior_latents