






'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__			=      logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   if "resnet-50" in model_name:
      lowercase_						:     List[str]							     =  ResNetConfig.from_pretrained("microsoft/resnet-50"				)
   elif "resnet-101" in model_name:
      lowercase_						:     Tuple							     =  ResNetConfig.from_pretrained("microsoft/resnet-101"				)
   else:
      raise ValueError("Model name should include either resnet50 or resnet101"				)

   lowercase_						:     List[Any]							     =  DetrConfig(use_timm_backbone=_UpperCamelCase    ,		backbone_config=_UpperCamelCase				)

   # set label attributes
   lowercase_						:     Optional[int]							     =  "panoptic" in model_name
   if is_panoptic:
      lowercase_						:     str							     =  250
   else:
      lowercase_						:     Union[str, Any]							     =  91
      lowercase_						:     str							     =  "huggingface/label-files"
      lowercase_						:     Optional[Any]							     =  "coco-detection-id2label.json"
      lowercase_						:     Optional[int]							     =  json.load(open(hf_hub_download(_UpperCamelCase    ,		_UpperCamelCase    ,		repo_type="dataset"				)    ,		"r"				)				)
      lowercase_						:     int							     =  {int(_UpperCamelCase				): v for k, v in idalabel.items()}
      lowercase_						:     str							     =  idalabel
      lowercase_						:     Optional[Any]							     =  {v: k for k, v in idalabel.items()}

   return config, is_panoptic




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  []

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")				)
   rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")				)
   rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")				)
   rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")				)
   rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")				)
   # stages
   for stage_idx in range(len(config.backbone_config.depths				)				):
      for layer_idx in range(config.backbone_config.depths[stage_idx]				):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                )				)
         # 3 convs
         for i in range(3				):
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                )				)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                )				)
    # fmt: on

   for i in range(config.encoder_layers				):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
              F"""encoder.layers.{i}.self_attn.out_proj.weight""",
          )				)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")				)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")				)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")				)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")				)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")				)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")				)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")				)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")				)
      rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")				)
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
              F"""decoder.layers.{i}.self_attn.out_proj.weight""",
          )				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")				)
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          )				)
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
          )				)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")				)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")				)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")				)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")				)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")				)
      rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")				)

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ("input_proj.weight", "input_projection.weight"),
           ("input_proj.bias", "input_projection.bias"),
           ("query_embed.weight", "query_position_embeddings.weight"),
           ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
           ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
           ("class_embed.weight", "class_labels_classifier.weight"),
           ("class_embed.bias", "class_labels_classifier.bias"),
           ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
           ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
           ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
           ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
           ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
           ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
       ]				)

   return rename_keys




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  state_dict.pop(_UpperCamelCase				)
   lowercase_						:     Dict							     =  val




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=False				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  ""
   if is_panoptic:
      lowercase_						:     Dict							     =  "detr."

   # first: transformer encoder
   for i in range(6				):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      lowercase_						:     Dict							     =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
      lowercase_						:     Optional[int]							     =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
      # next, add query, keys and values (in that order) to the state dict
      lowercase_						:     Optional[Any]							     =  in_proj_weight[:256, :]
      lowercase_						:     str							     =  in_proj_bias[:256]
      lowercase_						:     Tuple							     =  in_proj_weight[256:512, :]
      lowercase_						:     List[Any]							     =  in_proj_bias[256:512]
      lowercase_						:     Optional[int]							     =  in_proj_weight[-256:, :]
      lowercase_						:     Optional[int]							     =  in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6				):
      # read in weights + bias of input projection layer of self-attention
      lowercase_						:     Tuple							     =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
      lowercase_						:     Dict							     =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
      # next, add query, keys and values (in that order) to the state dict
      lowercase_						:     Union[str, Any]							     =  in_proj_weight[:256, :]
      lowercase_						:     Optional[Any]							     =  in_proj_bias[:256]
      lowercase_						:     Union[str, Any]							     =  in_proj_weight[256:512, :]
      lowercase_						:     Optional[int]							     =  in_proj_bias[256:512]
      lowercase_						:     List[Any]							     =  in_proj_weight[-256:, :]
      lowercase_						:     Union[str, Any]							     =  in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowercase_						:     str							     =  state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
      lowercase_						:     str							     =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      lowercase_						:     Optional[Any]							     =  in_proj_weight_cross_attn[:256, :]
      lowercase_						:     List[str]							     =  in_proj_bias_cross_attn[:256]
      lowercase_						:     Optional[int]							     =  in_proj_weight_cross_attn[256:512, :]
      lowercase_						:     List[str]							     =  in_proj_bias_cross_attn[256:512]
      lowercase_						:     Optional[Any]							     =  in_proj_weight_cross_attn[-256:, :]
      lowercase_						:     Optional[int]							     =  in_proj_bias_cross_attn[-256:]




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     int							     =  "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_						:     Any							     =  Image.open(requests.get(_UpperCamelCase    ,		stream=_UpperCamelCase				).raw				)

   return im




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=False				):


   """simple docstring"""




   lowercase_     ,		lowercase_						:     Any							     =  get_detr_config(_UpperCamelCase				)

   # load original model from torch hub
   lowercase_						:     Any							     =  {
       "detr-resnet-50": "detr_resnet50",
       "detr-resnet-101": "detr_resnet101",
   }
   logger.info(F"""Converting model {model_name}..."""				)
   lowercase_						:     Dict							     =  torch.hub.load("facebookresearch/detr"    ,		model_name_to_original_name[model_name]    ,		pretrained=_UpperCamelCase				).eval()
   lowercase_						:     int							     =  detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(_UpperCamelCase				):
      if is_panoptic:
         lowercase_						:     List[str]							     =  "detr." + src
      rename_key(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   # query, key and value matrices need special treatment
   read_in_q_k_v(_UpperCamelCase    ,		is_panoptic=_UpperCamelCase				)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowercase_						:     List[str]							     =  "detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("detr"				)
             and not key.startswith("class_labels_classifier"				)
             and not key.startswith("bbox_predictor"				)
         ):
            lowercase_						:     int							     =  state_dict.pop(_UpperCamelCase				)
            lowercase_						:     Union[str, Any]							     =  val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowercase_						:     int							     =  state_dict.pop(_UpperCamelCase				)
            lowercase_						:     str							     =  val
         elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
            continue
         else:
            lowercase_						:     Union[str, Any]							     =  state_dict.pop(_UpperCamelCase				)
            lowercase_						:     List[Any]							     =  val
      else:
         if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
            lowercase_						:     str							     =  state_dict.pop(_UpperCamelCase				)
            lowercase_						:     Dict							     =  val

    # finally, create HuggingFace model and load state dict
   lowercase_						:     Dict							     =  DetrForSegmentation(_UpperCamelCase				) if is_panoptic else DetrForObjectDetection(_UpperCamelCase				)
   model.load_state_dict(_UpperCamelCase				)
   model.eval()

   # verify our conversion on an image
   lowercase_						:     Dict							     =  "coco_panoptic" if is_panoptic else "coco_detection"
   lowercase_						:     Optional[int]							     =  DetrImageProcessor(format=_UpperCamelCase				)

   lowercase_						:     Any							     =  processor(images=prepare_img()    ,		return_tensors="pt"				)
   lowercase_						:     int							     =  encoding["pixel_values"]

   lowercase_						:     Tuple							     =  detr(_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  model(_UpperCamelCase				)

   assert torch.allclose(outputs.logits    ,		original_outputs["pred_logits"]    ,		atol=1e-3				)
   assert torch.allclose(outputs.pred_boxes    ,		original_outputs["pred_boxes"]    ,		atol=1e-3				)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks    ,		original_outputs["pred_masks"]    ,		atol=1e-4				)
   print("Looks ok!"				)

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
      Path(_UpperCamelCase				).mkdir(exist_ok=_UpperCamelCase				)
      model.save_pretrained(_UpperCamelCase				)
      processor.save_pretrained(_UpperCamelCase				)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info("Uploading PyTorch model and image processor to the hub..."				)
      model.push_to_hub(F"""nielsr/{model_name}"""				)
      processor.push_to_hub(F"""nielsr/{model_name}"""				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     default='detr-resnet-50',
     type=str,
     choices=['detr-resnet-50', 'detr-resnet-101'],
     help='Name of the DETR model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
 UpperCamelCase__			=      parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  hidden_states.shape
       lowercase_						:     Tuple							     =  jax.image.resize(
           a	,  shape=(batch, height * 2, width * 2, channels)	,  method="nearest"	,  )
       lowercase_						:     List[Any]							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(2, 2)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: int									=     None
    __lowerCamelCase: float									=     0.0
    __lowerCamelCase: bool									=     None
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.in_channels if self.out_channels is None else self.out_channels

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Tuple							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     List[str]							     =  nn.Dense(a	,  dtype=self.dtype      )

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Any							     =  nn.Dropout(self.dropout_prob      )
       lowercase_						:     Dict							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     Tuple							     =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowercase_						:     Optional[Any]							     =  None
       if use_nin_shortcut:
          lowercase_						:     Union[str, Any]							     =  nn.Conv(
              a	,  kernel_size=(1, 1)	,  strides=(1, 1)	,  padding="VALID"	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							str	,  a	:							Dict	,  a	:							List[str]=True      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  hidden_states
       lowercase_						:     int							     =  self.norma(a      )
       lowercase_						:     List[Any]							     =  nn.swish(a      )
       lowercase_						:     Dict							     =  self.conva(a      )

       lowercase_						:     Optional[int]							     =  self.time_emb_proj(nn.swish(a      )      )
       lowercase_						:     Tuple							     =  jnp.expand_dims(jnp.expand_dims(a	,  1      )	,  1      )
       lowercase_						:     List[str]							     =  hidden_states + temb

       lowercase_						:     Optional[Any]							     =  self.norma(a      )
       lowercase_						:     Any							     =  nn.swish(a      )
       lowercase_						:     List[str]							     =  self.dropout(a	,  a      )
       lowercase_						:     int							     =  self.conva(a      )

       if self.conv_shortcut is not None:
          lowercase_						:     List[str]							     =  self.conv_shortcut(a      )

       return hidden_states + residual




