
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_A   =     []
for i in range(6):
			# 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 encoder + 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.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("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"),
    ]
)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	state_dict.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowerCAmelCase_						=	key.replace("backbone.0.body"    ,							"backbone.conv_encoder.model"				)
									lowerCAmelCase_						=	value
						else:
									lowerCAmelCase_						=	value

			return new_state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	""""""

			# 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)
						lowerCAmelCase_						=	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
						lowerCAmelCase_						=	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
						lowerCAmelCase_						=	in_proj_weight[:256, :]
						lowerCAmelCase_						=	in_proj_bias[:256]
						lowerCAmelCase_						=	in_proj_weight[256:512, :]
						lowerCAmelCase_						=	in_proj_bias[256:512]
						lowerCAmelCase_						=	in_proj_weight[-256:, :]
						lowerCAmelCase_						=	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
						lowerCAmelCase_						=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
						lowerCAmelCase_						=	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
						lowerCAmelCase_						=	in_proj_weight[:256, :]
						lowerCAmelCase_						=	in_proj_bias[:256]
						lowerCAmelCase_						=	in_proj_weight[256:512, :]
						lowerCAmelCase_						=	in_proj_bias[256:512]
						lowerCAmelCase_						=	in_proj_weight[-256:, :]
						lowerCAmelCase_						=	in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCAmelCase_						=	state_dict.pop(
						    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
						lowerCAmelCase_						=	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
						lowerCAmelCase_						=	in_proj_weight_cross_attn[:256, :]
						lowerCAmelCase_						=	in_proj_bias_cross_attn[:256]
						lowerCAmelCase_						=	in_proj_weight_cross_attn[256:512, :]
						lowerCAmelCase_						=	in_proj_bias_cross_attn[256:512]
						lowerCAmelCase_						=	in_proj_weight_cross_attn[-256:, :]
						lowerCAmelCase_						=	in_proj_bias_cross_attn[-256:]






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	image.size
			lowerCAmelCase_						=	max(__lowerCAmelCase    ,							__lowerCAmelCase				)
			lowerCAmelCase_						=	800 if """detection""" in checkpoint_url else 1000
			lowerCAmelCase_						=	target_max_size / current_max_size
			lowerCAmelCase_						=	image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

			return resized_image






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			lowerCAmelCase_						=	F.to_tensor(__lowerCAmelCase				)
			lowerCAmelCase_						=	F.normalize(__lowerCAmelCase    ,							mean=[0.485, 0.456, 0.406]    ,							std=[0.229, 0.224, 0.225]				)
			return image






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			logger.info("Converting model..."				)

			# load original state dict
			lowerCAmelCase_						=	torch.hub.load_state_dict_from_url(__lowerCAmelCase    ,							map_location="cpu"				)
			# rename keys
			for src, dest in rename_keys:
						rename_key(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
			lowerCAmelCase_						=	rename_backbone_keys(__lowerCAmelCase				)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__lowerCAmelCase				)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			lowerCAmelCase_						=	"""model."""
			for key in state_dict.copy().keys():
						if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
									lowerCAmelCase_						=	state_dict.pop(__lowerCAmelCase				)
									lowerCAmelCase_						=	val
									 # create HuggingFace model and load state dict
			lowerCAmelCase_						=	TableTransformerConfig(
			    backbone="resnet18"    ,							mask_loss_coefficient=1    ,							dice_loss_coefficient=1    ,							ce_loss_coefficient=1    ,							bbox_loss_coefficient=5    ,							giou_loss_coefficient=2    ,							eos_coefficient=0.4    ,							class_cost=1    ,							bbox_cost=5    ,							giou_cost=2    ,							)

			if "detection" in checkpoint_url:
						lowerCAmelCase_						=	15
						lowerCAmelCase_						=	2
						lowerCAmelCase_						=	{0: """table""", 1: """table rotated"""}
						lowerCAmelCase_						=	idalabel
						lowerCAmelCase_						=	{v: k for k, v in idalabel.items()}
			else:
						lowerCAmelCase_						=	125
						lowerCAmelCase_						=	6
						lowerCAmelCase_						=	{
						    0: """table""",
						    1: """table column""",
						    2: """table row""",
						    3: """table column header""",
						    4: """table projected row header""",
						    5: """table spanning cell""",
						}
						lowerCAmelCase_						=	idalabel
						lowerCAmelCase_						=	{v: k for k, v in idalabel.items()}

			lowerCAmelCase_						=	DetrImageProcessor(
			    format="coco_detection"    ,							max_size=800 if "detection" in checkpoint_url else 1000				)
			lowerCAmelCase_						=	TableTransformerForObjectDetection(__lowerCAmelCase				)
			model.load_state_dict(__lowerCAmelCase				)
			model.eval()

			# verify our conversion
			lowerCAmelCase_						=	"""example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
			lowerCAmelCase_						=	hf_hub_download(repo_id="nielsr/example-pdf"    ,							repo_type="dataset"    ,							filename=__lowerCAmelCase				)
			lowerCAmelCase_						=	Image.open(__lowerCAmelCase				).convert("RGB"				)
			lowerCAmelCase_						=	normalize(resize(__lowerCAmelCase    ,							__lowerCAmelCase				)				).unsqueeze(0				)

			lowerCAmelCase_						=	model(__lowerCAmelCase				)

			if "detection" in checkpoint_url:
						lowerCAmelCase_						=	(1, 15, 3)
						lowerCAmelCase_						=	torch.tensor(
						    [[-6.7_897, -16.9985, 6.7_937], [-8.0_186, -22.2192, 6.9_677], [-7.3_117, -21.0708, 7.4_055]]				)
						lowerCAmelCase_						=	torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]				)

			else:
						lowerCAmelCase_						=	(1, 125, 7)
						lowerCAmelCase_						=	torch.tensor(
						    [[-18.1430, -8.3_214, 4.8_274], [-18.4685, -7.1_361, -4.2_667], [-26.3693, -9.3_429, -4.9_962]]				)
						lowerCAmelCase_						=	torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]				)

			assert outputs.logits.shape == expected_shape
			assert torch.allclose(outputs.logits[0, :3, :3]    ,							__lowerCAmelCase    ,							atol=1e-4				)
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,							__lowerCAmelCase    ,							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(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
						model.save_pretrained(__lowerCAmelCase				)
						image_processor.save_pretrained(__lowerCAmelCase				)

			if push_to_hub:
						# Push model to HF hub
						logger.info("Pushing model to the hub..."				)
						lowerCAmelCase_						=	(
						    """microsoft/table-transformer-detection"""
						    if """detection""" in checkpoint_url
						    else """microsoft/table-transformer-structure-recognition"""
						)
						model.push_to_hub(__lowerCAmelCase				)
						image_processor.push_to_hub(__lowerCAmelCase				)


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

			parser.add_argument(
			    "--checkpoint_url",
			    default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
			    type=str,
			    choices=[
			        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
			        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
			    ],
			    help="URL of the Table Transformer checkpoint 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_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    List[Any]           =			"""gpt_neo"""
      _UpperCAmelCase				:    Union[str, Any]           =			["""past_key_values"""]
      _UpperCAmelCase				:    List[Any]           =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(      self    ,  __magic_name__=5_0_2_5_7    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_4    ,  __magic_name__=[[["global", "local"], 1_2]]    ,  __magic_name__=1_6    ,  __magic_name__=None    ,  __magic_name__=2_5_6    ,  __magic_name__="gelu_new"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=1e-5    ,  __magic_name__=0.02    ,  __magic_name__=True    ,  __magic_name__=5_0_2_5_6    ,  __magic_name__=5_0_2_5_6    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_layers
       lowerCamelCase		:	str							=      num_heads
       lowerCamelCase		:	Optional[Any]							=      intermediate_size
       lowerCamelCase		:	List[Any]							=      window_size
       lowerCamelCase		:	int							=      activation_function
       lowerCamelCase		:	Union[str, Any]							=      resid_dropout
       lowerCamelCase		:	List[Any]							=      embed_dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Dict							=      classifier_dropout
       lowerCamelCase		:	Any							=      layer_norm_epsilon
       lowerCamelCase		:	Dict							=      initializer_range
       lowerCamelCase		:	Dict							=      use_cache

       lowerCamelCase		:	Optional[Any]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id

       lowerCamelCase		:	List[Any]							=      attention_types
       lowerCamelCase		:	Optional[Any]							=      self.expand_attention_types_params(__magic_name__      )

       if len(self.attention_layers      ) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            F'''but is `len(config.attention_layers) = {len(self.attention_layers      )}`, '''
            F'''`config.num_layers = {self.num_layers}`. '''
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""      )

       super().__init__(bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )






      @staticmethod
      def 						UpperCamelCase__							(      __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      []
       for item in attention_types:
        for _ in range(item[1]      ):
         attentions.extend(item[0]      )
       return attentions





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	Any							=      input.size()
 lowerCamelCase		:	List[Any]							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      shape[dimension]

 lowerCamelCase		:	Optional[int]							=      torch.arange(0,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Dict							=      torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="""floor"""							) + 1
 lowerCamelCase		:	int							=      torch.arange(lowerCamelCase							) + low_indices[:min_length][:, None]

 lowerCamelCase		:	str							=      [slice(lowerCamelCase							)] * rank
 lowerCamelCase		:	List[str]							=      indices
 lowerCamelCase		:	Dict							=      input[s]

 lowerCamelCase		:	Any							=      list(range(0,	rank + 1							)							)
 perm.append(perm.pop(dimension + 1							)							)

 return sliced.permute(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	List[Any]							=      torch.arange(1,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      torch.remainder(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      remainders == 0
 lowerCamelCase		:	List[Any]							=      candidates[divisor_indices]
 lowerCamelCase		:	Optional[Any]							=      torch.max(lowerCamelCase							)
 return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="""floor"""							)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
       if self.use_past:
        self.fill_with_past_key_values_(__magic_name__    ,  direction="""inputs"""      )
        lowerCamelCase		:	int							=      {0: """batch""", 1: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:	Tuple							=      {0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return self._config.num_heads
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	Optional[int]							=      super(__magic_name__    ,  self      ).generate_dummy_inputs(
           __magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )

       # We need to order the input in the way they appears in the forward()
       lowerCamelCase		:	int							=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
        else:
         import torch

         lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         lowerCamelCase		:	Optional[int]							=      seqlen + 2
         lowerCamelCase		:	List[Any]							=      (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowerCamelCase		:	str							=      [
             (torch.zeros(__magic_name__      ), torch.zeros(__magic_name__      )) for _ in range(self.num_layers      )
         ]

       lowerCamelCase		:	Tuple							=      common_inputs["""attention_mask"""]
       if self.use_past:
        lowerCamelCase		:	str							=      ordered_inputs["""attention_mask"""].dtype
        lowerCamelCase		:	Any							=      torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(__magic_name__    ,  __magic_name__    ,  dtype=__magic_name__      )]    ,  dim=1      )

       return ordered_inputs






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_3




