



'''simple docstring'''






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_							: int						 =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_							: Dict						 =	[]
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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = state_dict.pop(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[Any]			     = val




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _UpperCAmelCase  :						Union[str, Any]			     = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""		)
                     _UpperCAmelCase  :						Dict			     = value
              else:
                     _UpperCAmelCase  :						Dict			     = value

       return new_state_dict




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = """"""

       # 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)
              _UpperCAmelCase  :						str			     = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
              _UpperCAmelCase  :						List[Any]			     = 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
              _UpperCAmelCase  :						List[Any]			     = in_proj_weight[:256, :]
              _UpperCAmelCase  :						Any			     = in_proj_bias[:256]
              _UpperCAmelCase  :						Dict			     = in_proj_weight[256:512, :]
              _UpperCAmelCase  :						Optional[Any]			     = in_proj_bias[256:512]
              _UpperCAmelCase  :						Optional[int]			     = in_proj_weight[-256:, :]
              _UpperCAmelCase  :						List[Any]			     = 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
              _UpperCAmelCase  :						Optional[int]			     = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
              _UpperCAmelCase  :						int			     = 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
              _UpperCAmelCase  :						List[Any]			     = in_proj_weight[:256, :]
              _UpperCAmelCase  :						Union[str, Any]			     = in_proj_bias[:256]
              _UpperCAmelCase  :						Union[str, Any]			     = in_proj_weight[256:512, :]
              _UpperCAmelCase  :						Dict			     = in_proj_bias[256:512]
              _UpperCAmelCase  :						int			     = in_proj_weight[-256:, :]
              _UpperCAmelCase  :						Any			     = in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              _UpperCAmelCase  :						Optional[Any]			     = state_dict.pop(
                  F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''		)
              _UpperCAmelCase  :						List[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
              _UpperCAmelCase  :						Tuple			     = in_proj_weight_cross_attn[:256, :]
              _UpperCAmelCase  :						List[str]			     = in_proj_bias_cross_attn[:256]
              _UpperCAmelCase  :						Optional[int]			     = in_proj_weight_cross_attn[256:512, :]
              _UpperCAmelCase  :						List[Any]			     = in_proj_bias_cross_attn[256:512]
              _UpperCAmelCase  :						int			     = in_proj_weight_cross_attn[-256:, :]
              _UpperCAmelCase  :						Dict			     = in_proj_bias_cross_attn[-256:]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       _UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = image.size
       _UpperCAmelCase  :						List[Any]			     = max(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						Tuple			     = 800 if """detection""" in checkpoint_url else 1000
       _UpperCAmelCase  :						int			     = target_max_size / current_max_size
       _UpperCAmelCase  :						List[Any]			     = image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

       return resized_image




def     snake_case_					(     lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = F.to_tensor(lowerCAmelCase_		)
       _UpperCAmelCase  :						Dict			     = F.normalize(lowerCAmelCase_       ,   mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]       ,   std=[0.2_2_9, 0.2_2_4, 0.2_2_5]		)
       return image




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


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

       # load original state dict
       _UpperCAmelCase  :						str			     = torch.hub.load_state_dict_from_url(lowerCAmelCase_       ,   map_location="""cpu"""		)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						Tuple			     = 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
       _UpperCAmelCase  :						Tuple			     = """model."""
       for key in state_dict.copy().keys():
              if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
                     _UpperCAmelCase  :						Optional[Any]			     = state_dict.pop(lowerCAmelCase_		)
                     _UpperCAmelCase  :						Dict			     = val
    # create HuggingFace model and load state dict
       _UpperCAmelCase  :						Optional[int]			     = 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:
              _UpperCAmelCase  :						Tuple			     = 15
              _UpperCAmelCase  :						Tuple			     = 2
              _UpperCAmelCase  :						Tuple			     = {0: """table""", 1: """table rotated"""}
              _UpperCAmelCase  :						Union[str, Any]			     = idalabel
              _UpperCAmelCase  :						Optional[Any]			     = {v: k for k, v in idalabel.items()}
       else:
              _UpperCAmelCase  :						List[str]			     = 125
              _UpperCAmelCase  :						Any			     = 6
              _UpperCAmelCase  :						Any			     = {
                  0: """table""",
                  1: """table column""",
                  2: """table row""",
                  3: """table column header""",
                  4: """table projected row header""",
                  5: """table spanning cell""",
              }
              _UpperCAmelCase  :						List[Any]			     = idalabel
              _UpperCAmelCase  :						Union[str, Any]			     = {v: k for k, v in idalabel.items()}

       _UpperCAmelCase  :						str			     = DetrImageProcessor(
           format="""coco_detection"""       ,   max_size=800 if """detection""" in checkpoint_url else 1000		)
       _UpperCAmelCase  :						Optional[Any]			     = TableTransformerForObjectDetection(lowerCAmelCase_		)
       model.load_state_dict(lowerCAmelCase_		)
       model.eval()

       # verify our conversion
       _UpperCAmelCase  :						Dict			     = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
       _UpperCAmelCase  :						Optional[int]			     = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=lowerCAmelCase_		)
       _UpperCAmelCase  :						Union[str, Any]			     = Image.open(lowerCAmelCase_		).convert("""RGB"""		)
       _UpperCAmelCase  :						int			     = normalize(resize(lowerCAmelCase_       ,   lowerCAmelCase_		)		).unsqueeze(0		)

       _UpperCAmelCase  :						Dict			     = model(lowerCAmelCase_		)

       if "detection" in checkpoint_url:
              _UpperCAmelCase  :						str			     = (1, 15, 3)
              _UpperCAmelCase  :						Optional[int]			     = torch.tensor(
                  [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]		)
              _UpperCAmelCase  :						int			     = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]		)

       else:
              _UpperCAmelCase  :						Optional[int]			     = (1, 125, 7)
              _UpperCAmelCase  :						Any			     = torch.tensor(
                  [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]		)
              _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]		)

       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..."""		)
              _UpperCAmelCase  :						List[str]			     = (
                  """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_							: Tuple						 =	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_							: Union[str, Any]						 =	parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


A_							: Any						 =	logging.get_logger(__name__)

A_							: Optional[int]						 =	{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """longformer"""







 def __init__(							self  ,a_ = 512  ,a_ = 2  ,a_ = 1  ,a_ = 0  ,a_ = 2  ,a_ = 30_522  ,a_ = 768  ,a_ = 12  ,a_ = 12  ,a_ = 3_072  ,a_ = "gelu"  ,a_ = 0.1  ,a_ = 0.1  ,a_ = 512  ,a_ = 2  ,a_ = 0.02  ,a_ = 1E-1_2  ,a_ = False  ,**a_  ,)		->   List[Any]:
        super().__init__(pad_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						List[Any]			     = attention_window
        _UpperCAmelCase  :						Any			     = sep_token_id
        _UpperCAmelCase  :						Dict			     = bos_token_id
        _UpperCAmelCase  :						Tuple			     = eos_token_id
        _UpperCAmelCase  :						Tuple			     = vocab_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Union[str, Any]			     = num_attention_heads
        _UpperCAmelCase  :						Optional[int]			     = hidden_act
        _UpperCAmelCase  :						str			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						List[str]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[int]			     = type_vocab_size
        _UpperCAmelCase  :						Any			     = initializer_range
        _UpperCAmelCase  :						Optional[int]			     = layer_norm_eps
        _UpperCAmelCase  :						Union[str, Any]			     = onnx_export







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None							)		->   int:
        super().__init__(a_  ,a_  ,a_							)
        _UpperCAmelCase  :						Tuple			     = True







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase  :						Optional[int]			     = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase  :						Tuple			     = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""global_attention_mask""", dynamic_axis),
            ]							)







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						str			     = super().outputs
        if self.task == "default":
               _UpperCAmelCase  :						int			     = {0: """batch"""}
        return outputs







 @property
 def _snake_case (							self							)		->   float:
        return 1E-4







 @property
 def _snake_case (							self							)		->   int:
        # needs to be >= 14 to support tril operator
        return max(super().default_onnx_opset  ,14							)







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						List[str]			     = super().generate_dummy_inputs(
            preprocessor=a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_							)
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        _UpperCAmelCase  :						int			     = torch.zeros_like(inputs["""input_ids"""]							)
        # make every second token global
        _UpperCAmelCase  :						List[str]			     = 1

        return inputs



