"""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()
lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__  :  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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							state_dict.pop(lowerCamelCase       )
				UpperCAmelCase__             =							val



def   a_						(					lowerCamelCase       ):
				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       ):
				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       ):
				UpperCAmelCase__      ,	UpperCAmelCase__             =							image.size
				UpperCAmelCase__             =							max(lowerCamelCase					,			lowerCamelCase       )
				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       ):
				UpperCAmelCase__             =							F.to_tensor(lowerCamelCase       )
				UpperCAmelCase__             =							F.normalize(lowerCamelCase					,			mean=[0.485, 0.456, 0.406]					,			std=[0.229, 0.224, 0.225]       )
				return image



@torch.no_grad()
def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				logger.info('Converting model...'       )

				# load original state dict
				UpperCAmelCase__             =							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__             =							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__             =							'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(lowerCamelCase       )
												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(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				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=lowerCamelCase       )
				UpperCAmelCase__             =							Image.open(lowerCamelCase       ).convert('RGB'       )
				UpperCAmelCase__             =							normalize(resize(lowerCamelCase					,			lowerCamelCase       )       ).unsqueeze(0       )

				UpperCAmelCase__             =							model(lowerCamelCase       )

				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]					,			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__             =							(
								    '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__":
						lowerCAmelCase__  :  Dict						     =	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__  :  Optional[int]						     =	parser.parse_args()
						convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






import math



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * power_factor



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



