






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()
__magic_name__:					str							=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__:					Any							=     []
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    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= state_dict.pop(lowercase__			)
						__magic_name__      :       List[str]       						= val

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

						return new_state_dict

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







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

						return resized_image

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       List[str]       						= F.to_tensor(lowercase__			)
						__magic_name__      :       List[str]       						= F.normalize(lowercase__,   mean=[0.485, 0.456, 0.406],   std=[0.229, 0.224, 0.225]			)
						return image

@torch.no_grad()
def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







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

						# load original state dict
						__magic_name__      :       int       						= torch.hub.load_state_dict_from_url(lowercase__,   map_location="""cpu"""			)
						# rename keys
						for src, dest in rename_keys:
												rename_key(lowercase__,   lowercase__,   lowercase__			)
						__magic_name__      :       Tuple       						= rename_backbone_keys(lowercase__			)
						# query, key and value matrices need special treatment
						read_in_q_k_v(lowercase__			)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						__magic_name__      :       Optional[Any]       						= """model."""
						for key in state_dict.copy().keys():
												if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
																		__magic_name__      :       Union[str, Any]       						= state_dict.pop(lowercase__			)
																		__magic_name__      :       str       						= val
    # create HuggingFace model and load state dict
						__magic_name__      :       List[Any]       						= 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:
												__magic_name__      :       str       						= 15
												__magic_name__      :       Any       						= 2
												__magic_name__      :       Union[str, Any]       						= {0: """table""", 1: """table rotated"""}
												__magic_name__      :       List[Any]       						= idalabel
												__magic_name__      :       Dict       						= {v: k for k, v in idalabel.items()}
						else:
												__magic_name__      :       int       						= 125
												__magic_name__      :       Optional[Any]       						= 6
												__magic_name__      :       Optional[int]       						= {
												    0: """table""",
												    1: """table column""",
												    2: """table row""",
												    3: """table column header""",
												    4: """table projected row header""",
												    5: """table spanning cell""",
												}
												__magic_name__      :       Optional[int]       						= idalabel
												__magic_name__      :       List[Any]       						= {v: k for k, v in idalabel.items()}

						__magic_name__      :       int       						= DetrImageProcessor(
						    format="""coco_detection""",   max_size=800 if """detection""" in checkpoint_url else 1000			)
						__magic_name__      :       Any       						= TableTransformerForObjectDetection(lowercase__			)
						model.load_state_dict(lowercase__			)
						model.eval()

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

						__magic_name__      :       List[str]       						= model(lowercase__			)

						if "detection" in checkpoint_url:
												__magic_name__      :       List[str]       						= (1, 15, 3)
												__magic_name__      :       Optional[Any]       						= torch.tensor(
												    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]			)
												__magic_name__      :       Union[str, Any]       						= torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]			)

						else:
												__magic_name__      :       List[Any]       						= (1, 125, 7)
												__magic_name__      :       Dict       						= torch.tensor(
												    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]			)
												__magic_name__      :       Tuple       						= torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]			)

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

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


if __name__ == "__main__":
		__magic_name__:					Optional[Any]							=     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."
		)
		__magic_name__:					Tuple							=     parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import os
from pathlib import Path

def    UpperCamelCase						(	):






						"""simple docstring"""







						from torch.utils.cpp_extension import load

						__magic_name__      :       Dict       						= Path(_A			).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
						__magic_name__      :       Optional[int]       						= [
						    root / filename
						    for filename in [
						        """vision.cpp""",
						        os.path.join("""cpu""",   """ms_deform_attn_cpu.cpp"""			),
						        os.path.join("""cuda""",   """ms_deform_attn_cuda.cu"""			),
						    ]
						]

						load(
						    """MultiScaleDeformableAttention""",   _A,   with_cuda=_A,   extra_include_paths=[str(_A			)],   extra_cflags=["""-DWITH_CUDA=1"""],   extra_cuda_cflags=[
						        """-DCUDA_HAS_FP16=1""",
						        """-D__CUDA_NO_HALF_OPERATORS__""",
						        """-D__CUDA_NO_HALF_CONVERSIONS__""",
						        """-D__CUDA_NO_HALF2_OPERATORS__""",
						    ],   )

						import MultiScaleDeformableAttention as MSDA

						return MSDA
