
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()
lowerCamelCase__						=				logging.get_logger(__name__)

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

		"""simple docstring"""






		_UpperCamelCase         =						state_dict.pop(_a			)
		_UpperCamelCase         =						val


def 				__A(lowerCAmelCase			)		->					List[Any]:

		"""simple docstring"""






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

		return new_state_dict


def 				__A(lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






		_UpperCamelCase         =						""""""

		# 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         =						state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
				_UpperCamelCase         =						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         =						in_proj_weight[:2_5_6, :]
				_UpperCamelCase         =						in_proj_bias[:2_5_6]
				_UpperCamelCase         =						in_proj_weight[2_5_6:5_1_2, :]
				_UpperCamelCase         =						in_proj_bias[2_5_6:5_1_2]
				_UpperCamelCase         =						in_proj_weight[-2_5_6:, :]
				_UpperCamelCase         =						in_proj_bias[-2_5_6:]
		# 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         =						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
				_UpperCamelCase         =						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         =						in_proj_weight[:2_5_6, :]
				_UpperCamelCase         =						in_proj_bias[:2_5_6]
				_UpperCamelCase         =						in_proj_weight[2_5_6:5_1_2, :]
				_UpperCamelCase         =						in_proj_bias[2_5_6:5_1_2]
				_UpperCamelCase         =						in_proj_weight[-2_5_6:, :]
				_UpperCamelCase         =						in_proj_bias[-2_5_6:]
				# read in weights + bias of input projection layer of cross-attention
				_UpperCamelCase         =						state_dict.pop(
				    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
				_UpperCamelCase         =						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         =						in_proj_weight_cross_attn[:2_5_6, :]
				_UpperCamelCase         =						in_proj_bias_cross_attn[:2_5_6]
				_UpperCamelCase         =						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
				_UpperCamelCase         =						in_proj_bias_cross_attn[2_5_6:5_1_2]
				_UpperCamelCase         =						in_proj_weight_cross_attn[-2_5_6:, :]
				_UpperCamelCase         =						in_proj_bias_cross_attn[-2_5_6:]


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					List[str]:

		"""simple docstring"""






		_UpperCamelCase     ,						_UpperCamelCase         =						image.size
		_UpperCamelCase         =						max(_a    ,	_a			)
		_UpperCamelCase         =						8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
		_UpperCamelCase         =						target_max_size / current_max_size
		_UpperCamelCase         =						image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

		return resized_image


def 				__A(lowerCAmelCase			)		->					int:

		"""simple docstring"""






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


@torch.no_grad()
def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)		->					Optional[int]:

		"""simple docstring"""






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

		# load original state dict
		_UpperCamelCase         =						torch.hub.load_state_dict_from_url(_a    ,	map_location="""cpu"""			)
		# rename keys
		for src, dest in rename_keys:
				rename_key(_a    ,	_a    ,	_a			)
		_UpperCamelCase         =						rename_backbone_keys(_a			)
		# query, key and value matrices need special treatment
		read_in_q_k_v(_a			)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_UpperCamelCase         =						"""model."""
		for key in state_dict.copy().keys():
				if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
						_UpperCamelCase         =						state_dict.pop(_a			)
						_UpperCamelCase         =						val
    # create HuggingFace model and load state dict
		_UpperCamelCase         =						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         =						1_5
				_UpperCamelCase         =						2
				_UpperCamelCase         =						{0: """table""", 1: """table rotated"""}
				_UpperCamelCase         =						idalabel
				_UpperCamelCase         =						{v: k for k, v in idalabel.items()}
		else:
				_UpperCamelCase         =						1_2_5
				_UpperCamelCase         =						6
				_UpperCamelCase         =						{
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				_UpperCamelCase         =						idalabel
				_UpperCamelCase         =						{v: k for k, v in idalabel.items()}

		_UpperCamelCase         =						DetrImageProcessor(
		    format="""coco_detection"""    ,	max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0			)
		_UpperCamelCase         =						TableTransformerForObjectDetection(_a			)
		model.load_state_dict(_a			)
		model.eval()

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

		_UpperCamelCase         =						model(_a			)

		if "detection" in checkpoint_url:
				_UpperCamelCase         =						(1, 1_5, 3)
				_UpperCamelCase         =						torch.tensor(
				    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]			)
				_UpperCamelCase         =						torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]			)

		else:
				_UpperCamelCase         =						(1, 1_2_5, 7)
				_UpperCamelCase         =						torch.tensor(
				    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]			)
				_UpperCamelCase         =						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]    ,	_a    ,	atol=1e-4			)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,	_a    ,	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(_a			).mkdir(exist_ok=_a			)
				model.save_pretrained(_a			)
				image_processor.save_pretrained(_a			)

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


if __name__ == "__main__":
		lowerCamelCase__						=				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."
		)
		lowerCamelCase__						=				parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




