



"""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()
UpperCAmelCase						:		Optional[Any]			= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase						:		Tuple			= []
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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[Any]:







		'''simple docstring'''


		lowercase_								= state_dict.pop(__lowerCAmelCase							)
		lowercase_								= val


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







		'''simple docstring'''


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

		return new_state_dict


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







		'''simple docstring'''


		lowercase_								= """"""

		# 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)
				lowercase_								= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
				lowercase_								= 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
				lowercase_								= in_proj_weight[:2_56, :]
				lowercase_								= in_proj_bias[:2_56]
				lowercase_								= in_proj_weight[2_56:5_12, :]
				lowercase_								= in_proj_bias[2_56:5_12]
				lowercase_								= in_proj_weight[-2_56:, :]
				lowercase_								= in_proj_bias[-2_56:]
		# 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
				lowercase_								= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
				lowercase_								= 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
				lowercase_								= in_proj_weight[:2_56, :]
				lowercase_								= in_proj_bias[:2_56]
				lowercase_								= in_proj_weight[2_56:5_12, :]
				lowercase_								= in_proj_bias[2_56:5_12]
				lowercase_								= in_proj_weight[-2_56:, :]
				lowercase_								= in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase_								= state_dict.pop(
				    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
				lowercase_								= 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
				lowercase_								= in_proj_weight_cross_attn[:2_56, :]
				lowercase_								= in_proj_bias_cross_attn[:2_56]
				lowercase_								= in_proj_weight_cross_attn[2_56:5_12, :]
				lowercase_								= in_proj_bias_cross_attn[2_56:5_12]
				lowercase_								= in_proj_weight_cross_attn[-2_56:, :]
				lowercase_								= in_proj_bias_cross_attn[-2_56:]


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						List[Any]:







		'''simple docstring'''


		lowercase_					,    lowercase_								= image.size
		lowercase_								= max(__lowerCAmelCase ,  __lowerCAmelCase							)
		lowercase_								= 8_00 if """detection""" in checkpoint_url else 10_00
		lowercase_								= target_max_size / current_max_size
		lowercase_								= image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

		return resized_image


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







		'''simple docstring'''


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


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







		'''simple docstring'''


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

		# load original state dict
		lowercase_								= torch.hub.load_state_dict_from_url(__lowerCAmelCase ,  map_location="""cpu"""							)
		# rename keys
		for src, dest in rename_keys:
				rename_key(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
		lowercase_								= 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
		lowercase_								= """model."""
		for key in state_dict.copy().keys():
				if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
						lowercase_								= state_dict.pop(__lowerCAmelCase							)
						lowercase_								= val
    # create HuggingFace model and load state dict
		lowercase_								= 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:
				lowercase_								= 15
				lowercase_								= 2
				lowercase_								= {0: """table""", 1: """table rotated"""}
				lowercase_								= idalabel
				lowercase_								= {v: k for k, v in idalabel.items()}
		else:
				lowercase_								= 1_25
				lowercase_								= 6
				lowercase_								= {
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				lowercase_								= idalabel
				lowercase_								= {v: k for k, v in idalabel.items()}

		lowercase_								= DetrImageProcessor(
		    format="""coco_detection""" ,  max_size=8_00 if """detection""" in checkpoint_url else 10_00							)
		lowercase_								= TableTransformerForObjectDetection(__lowerCAmelCase							)
		model.load_state_dict(__lowerCAmelCase							)
		model.eval()

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

		lowercase_								= model(__lowerCAmelCase							)

		if "detection" in checkpoint_url:
				lowercase_								= (1, 15, 3)
				lowercase_								= torch.tensor(
				    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]							)
				lowercase_								= 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:
				lowercase_								= (1, 1_25, 7)
				lowercase_								= torch.tensor(
				    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]							)
				lowercase_								= 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..."""							)
				lowercase_								= (
				    """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__":
							UpperCAmelCase						:		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."
							)
							UpperCAmelCase						:		Optional[Any]			= parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


  return (data["data"], data["target"])


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


  lowercase_								= XGBRegressor(verbosity=0 ,  random_state=42							)
  xgb.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Predict target for test data
  lowercase_								= xgb.predict(__lowerCAmelCase							)
  lowercase_								= predictions.reshape(len(__lowerCAmelCase							) ,  1							)
  return predictions


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= xgboost(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)
  print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

