



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()
_snake_case		=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case		=  []
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 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    val






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							return new_state_dict






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    ""

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    image.size
							_lowerCAmelCase     :							Union[str, Any]    =    max(_lowerCamelCase				, _lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    800 if "detection" in checkpoint_url else 1_000
							_lowerCAmelCase     :							Optional[int]    =    target_max_size / current_max_size
							_lowerCAmelCase     :							Optional[Any]    =    image.resize((int(round(scale * width  )  ), int(round(scale * height  )  ))  )

							return resized_image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    F.to_tensor(_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[Any]    =    F.normalize(_lowerCamelCase				, mean=[0.4_85, 0.4_56, 0.4_06]				, std=[0.2_29, 0.2_24, 0.2_25]  )
							return image






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




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

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

							_lowerCAmelCase     :							List[Any]    =    DetrImageProcessor(
							    format="coco_detection"				, max_size=800 if "detection" in checkpoint_url else 1_000  )
							_lowerCAmelCase     :							Tuple    =    TableTransformerForObjectDetection(_lowerCamelCase  )
							model.load_state_dict(_lowerCamelCase  )
							model.eval()

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

							_lowerCAmelCase     :							List[str]    =    model(_lowerCamelCase  )

							if "detection" in checkpoint_url:
														_lowerCAmelCase     :							Optional[Any]    =    (1, 15, 3)
														_lowerCAmelCase     :							Optional[int]    =    torch.tensor(
														    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]  )
														_lowerCAmelCase     :							Any    =    torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]  )

							else:
														_lowerCAmelCase     :							Optional[int]    =    (1, 125, 7)
														_lowerCAmelCase     :							int    =    torch.tensor(
														    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]  )
														_lowerCAmelCase     :							List[Any]    =    torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]  )

							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     :							Tuple    =    (
														    "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__":
						_snake_case		=  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."
						)
						_snake_case		=  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 Mapping

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


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)