code: 
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
  from PIL import Image
  
  from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _A							    =	logging.get_logger(__name__)
  def 						lowerCAmelCase_       (	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =DetaConfig(
  							    num_queries=900	,	encoder_ffn_dim=2048	,	decoder_ffn_dim=2048	,	num_feature_levels=5	,	assign_first_stage=__a	,	with_box_refine=__a	,	two_stage=__a	,	)
  
  							# set labels
  							SCREAMING_SNAKE_CASE			: Dict	     =91
  							SCREAMING_SNAKE_CASE			: Dict	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: List[str]	     ='''coco-detection-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =json.load(open(cached_download(hf_hub_url(__a	,	__a	,	repo_type='''dataset''' ) )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: int	     ={int(__a ): v for k, v in idalabel.items()}
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Any	     ={v: k for k, v in idalabel.items()}
  
  							return config
  def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  
  							# stem
  							# fmt: off
  							rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''') )
  							rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''') )
  							rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''') )
  							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''') )
  							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.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'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
  																												    ) )
      # transformer encoder
  							for i in range(config.encoder_layers ):
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight') )
  														rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias') )
  
  							# transformer decoder
  							for i in range(config.decoder_layers ):
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight') )
  														rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias') )
  
  							# fmt: on
  
  							return rename_keys
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =dct.pop(__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =val
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =config.d_model
  							for i in range(config.decoder_layers ):
  														# read in weights + bias of input projection layer of self-attention
  														SCREAMING_SNAKE_CASE			: List[str]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
  														# next, add query, keys and values (in that order) to the state dict
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_weight[:hidden_size, :]
  														SCREAMING_SNAKE_CASE			: List[Any]	     =in_proj_bias[:hidden_size]
  														SCREAMING_SNAKE_CASE			: int	     =in_proj_weight[
  														    hidden_size : hidden_size * 2, :
  														]
  														SCREAMING_SNAKE_CASE			: Any	     =in_proj_bias[hidden_size : hidden_size * 2]
  														SCREAMING_SNAKE_CASE			: List[str]	     =in_proj_weight[-hidden_size:, :]
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_bias[-hidden_size:]
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Image.open(requests.get(__a	,	stream=__a ).raw )
  
  							return im
  @torch.no_grad()
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =get_deta_config()
  
  							# load original state dict
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: int	     ='''adet_checkpoint0011.pth'''
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: str	     ='''adet_2x_checkpoint0023.pth'''
  							else:
  														raise ValueError(f'Model name {model_name} not supported' )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,	filename=__a )
  							SCREAMING_SNAKE_CASE			: str	     =torch.load(__a	,	map_location='''cpu''' )['''model''']
  
  							# rename keys
  							SCREAMING_SNAKE_CASE			: int	     =create_rename_keys(__a )
  							for src, dest in rename_keys:
  														rename_key(__a	,	__a	,	__a )
  							read_in_decoder_q_k_v(__a	,	__a )
  
  							# fix some prefixes
  							for key in state_dict.copy().keys():
  														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
  																					SCREAMING_SNAKE_CASE			: str	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  														if "input_proj" in key:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =val
  														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
  																					SCREAMING_SNAKE_CASE			: Dict	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  
      # finally, create HuggingFace model and load state dict
  							SCREAMING_SNAKE_CASE			: str	     =DetaForObjectDetection(__a )
  							model.load_state_dict(__a )
  							model.eval()
  
  							SCREAMING_SNAKE_CASE			: int	     ='''cuda''' if torch.cuda.is_available() else '''cpu'''
  							model.to(__a )
  
  							# load image processor
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DetaImageProcessor(format='''coco_detection''' )
  
  							# verify our conversion on image
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =prepare_img()
  							SCREAMING_SNAKE_CASE			: Any	     =processor(images=__a	,	return_tensors='''pt''' )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =encoding['''pixel_values''']
  							SCREAMING_SNAKE_CASE			: Dict	     =model(pixel_values.to(__a ) )
  
  							# verify logits
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.tensor(
  														    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
  														SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: Any	     =torch.tensor(
  														    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )
  
  							assert torch.allclose(outputs.logits[0, :3, :3]	,	expected_logits.to(__a )	,	atol=1E-4 )
  							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(__a )	,	atol=1E-4 )
  							print('''Everything ok!''' )
  
  							if pytorch_dump_folder_path:
  														# Save model and processor
  														logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
  														Path(__a ).mkdir(exist_ok=__a )
  														model.save_pretrained(__a )
  														processor.save_pretrained(__a )
  
  							# Push to hub
  							if push_to_hub:
  														print('''Pushing model and processor to hub...''' )
  														model.push_to_hub(f'jozhang97/{model_name}' )
  														processor.push_to_hub(f'jozhang97/{model_name}' )
  
  
  if __name__ == "__main__":
  							_A							    =	argparse.ArgumentParser()
  
  							parser.add_argument(
  							    """--model_name""",
  							    type=str,
  							    default="""deta-resnet-50""",
  							    choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
  							    help="""Name of the 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 or not to push the converted model to the 🤗 hub."""
  							)
  							_A							    =	parser.parse_args()
  							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
  from ....modeling_utils import ModuleUtilsMixin
  from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	"""MMBTConfig"""
  
  
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      str:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: Dict	     =encoder
  											SCREAMING_SNAKE_CASE			: Tuple	     =nn.Linear(config.modal_hidden_size		,		config.hidden_size							)
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.position_embeddings
  											SCREAMING_SNAKE_CASE			: List[str]	     =embeddings.token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =embeddings.word_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.LayerNorm
  											SCREAMING_SNAKE_CASE			: Any	     =nn.Dropout(p=config.hidden_dropout_prob							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.proj_embeddings(self.encoder(snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =token_embeddings.size(1							)
  
  											if start_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.cat([start_token_embeds.unsqueeze(1							), token_embeddings]		,		dim=1							)
  
  											if end_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.cat([token_embeddings, end_token_embeds.unsqueeze(1							)]		,		dim=1							)
  
  											if position_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.arange(snake_case_		,		dtype=torch.long		,		device=input_modal.device							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =position_ids.unsqueeze(0							).expand(input_modal.size(0							)		,		snake_case_							)
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.zeros(
  																		    (input_modal.size(0							), seq_length)		,		dtype=torch.long		,		device=input_modal.device							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.position_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.token_type_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Dict	     =token_embeddings + position_embeddings + token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =self.LayerNorm(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.dropout(snake_case_							)
  											return embeddings
  
  
  
  
  
  
  
  _A							    =	r"""
      MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
      Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
      It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
      obtain state-of-the-art performance on various multimodal classification benchmark tasks.
  
      This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
      and behavior.
  
      Parameters:
          config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration.
          transformer (`nn.Module`): A text transformer that is used by MMBT.
              It should have embeddings, encoder, and pooler attributes.
          encoder (`nn.Module`): Encoder for the second modality.
              It should take in a batch of modal inputs and return k, n dimension embeddings.
  """
  
  _A							    =	r"""
      Args:
          input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
              The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
              Encoder, the shape would be (batch_size, channels, height, width)
          input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
              appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
              [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
              tasks.
          modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
          attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
              1]`:
  
              - 0 corresponds to a *sentence A* token,
              - 1 corresponds to a *sentence B* token.
  
              [What are token type IDs?](../glossary#token-type-ids)
          modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
              Segment token indices to indicate different portions of the non-text modality. The embeddings from these
              tokens will be summed with the respective token embeddings for the non-text modality.
          position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
              config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
              Selected in the range `[0, config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      'The bare MMBT Model outputting raw hidden-states without any specific head on top.'  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module  ,   UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Any:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: str	     =transformer
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =ModalEmbeddings(snake_case_		,		snake_case_		,		transformer.embeddings							)
  
  
  
  
  				@add_start_docstrings_to_model_forward(snake_case_							)
  				@replace_return_docstrings(output_type=snake_case_		,		config_class=_CONFIG_FOR_DOC							)
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =output_attentions if output_attentions is not None else self.config.output_attentions
  											SCREAMING_SNAKE_CASE			: int	     =(
  											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  											)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											if input_ids is not None and inputs_embeds is not None:
  																		raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''							)
  											elif input_ids is not None:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.size()
  											elif inputs_embeds is not None:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =inputs_embeds.size()[:-1]
  											else:
  																		raise ValueError('''You have to specify either input_ids or inputs_embeds'''							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.device if input_ids is not None else inputs_embeds.device
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.modal_encoder(
  											    snake_case_		,		start_token=snake_case_		,		end_token=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =modal_embeddings.size()[:-1]
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.ones(snake_case_		,		dtype=torch.long		,		device=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.transformer.embeddings(
  											    input_ids=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		inputs_embeds=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =torch.cat([modal_embeddings, txt_embeddings]		,		1							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =embedding_output.size()[:-1]
  
  											if attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_		,		dtype=torch.long							), attention_mask]		,		dim=1							)
  											if encoder_attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_							), encoder_attention_mask]		,		dim=1							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_extended_attention_mask(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.invert_attention_mask(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.get_head_mask(snake_case_		,		self.config.num_hidden_layers							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.transformer.encoder(
  											    snake_case_		,		attention_mask=snake_case_		,		head_mask=snake_case_		,		encoder_hidden_states=snake_case_		,		encoder_attention_mask=snake_case_		,		output_attentions=snake_case_		,		output_hidden_states=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =encoder_outputs[0]
  											SCREAMING_SNAKE_CASE			: str	     =self.transformer.pooler(snake_case_							)
  
  											if not return_dict:
  																		return (sequence_output, pooled_output) + encoder_outputs[1:]
  
  											return BaseModelOutputWithPooling(
  											    last_hidden_state=snake_case_		,		pooler_output=snake_case_		,		hidden_states=encoder_outputs.hidden_states		,		attentions=encoder_outputs.attentions		,		)
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											return self.embeddings.word_embeddings
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: str	     =value
  
  
  
  
  
  
  
  @add_start_docstrings(
      '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    '  ,   UpperCamelCase__  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      int:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: Dict	     =config.num_labels
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =MMBTModel(snake_case_		,		snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: int	     =nn.Dropout(config.hidden_dropout_prob							)
  											SCREAMING_SNAKE_CASE			: Dict	     =nn.Linear(config.hidden_size		,		config.num_labels							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Dict	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.mmbt(
  											    input_modal=snake_case_		,		input_ids=snake_case_		,		modal_start_tokens=snake_case_		,		modal_end_tokens=snake_case_		,		attention_mask=snake_case_		,		token_type_ids=snake_case_		,		modal_token_type_ids=snake_case_		,		position_ids=snake_case_		,		modal_position_ids=snake_case_		,		head_mask=snake_case_		,		inputs_embeds=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =outputs[1]
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.dropout(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.classifier(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =None
  											if labels is not None:
  																		if self.num_labels == 1:
  																									#  We are doing regression
  																									SCREAMING_SNAKE_CASE			: List[Any]	     =MSELoss()
  																									SCREAMING_SNAKE_CASE			: int	     =loss_fct(logits.view(-1							)		,		labels.view(-1							)							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: List[str]	     =CrossEntropyLoss()
  																									SCREAMING_SNAKE_CASE			: Dict	     =loss_fct(logits.view(-1		,		self.num_labels							)		,		labels.view(-1							)							)
  
  											if not return_dict:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =(logits,) + outputs[2:]
  																		return ((loss,) + output) if loss is not None else output
  
  											return SequenceClassifierOutput(
  											    loss=snake_case_		,		logits=snake_case_		,		hidden_states=outputs.hidden_states		,		attentions=outputs.attentions		,		)
  
  
  
  
  
  
  