


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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase    :     Union[str, 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      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      str       ,			_UpperCamelCase		:      Optional[Any]							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					state_dict.pop(_UpperCamelCase							)
							__UpperCAmelCase   :						str      							=					val
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       Tuple:

							'''simple docstring'''



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

							return new_state_dict
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       List[str]:

							'''simple docstring'''



							__UpperCAmelCase   :						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)
														__UpperCAmelCase   :						Optional[int]      							=					state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
														__UpperCAmelCase   :						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
														__UpperCAmelCase   :						List[str]      							=					in_proj_weight[:2_5_6, :]
														__UpperCAmelCase   :						str      							=					in_proj_bias[:2_5_6]
														__UpperCAmelCase   :						Tuple      							=					in_proj_weight[2_5_6:5_1_2, :]
														__UpperCAmelCase   :						Optional[int]      							=					in_proj_bias[2_5_6:5_1_2]
														__UpperCAmelCase   :						List[Any]      							=					in_proj_weight[-2_5_6:, :]
														__UpperCAmelCase   :						List[Any]      							=					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   :						str      							=					state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
														__UpperCAmelCase   :						Union[str, Any]      							=					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   :						Union[str, Any]      							=					in_proj_weight[:2_5_6, :]
														__UpperCAmelCase   :						List[Any]      							=					in_proj_bias[:2_5_6]
														__UpperCAmelCase   :						Tuple      							=					in_proj_weight[2_5_6:5_1_2, :]
														__UpperCAmelCase   :						Dict      							=					in_proj_bias[2_5_6:5_1_2]
														__UpperCAmelCase   :						Optional[Any]      							=					in_proj_weight[-2_5_6:, :]
														__UpperCAmelCase   :						Union[str, Any]      							=					in_proj_bias[-2_5_6:]
														# read in weights + bias of input projection layer of cross-attention
														__UpperCAmelCase   :						List[Any]      							=					state_dict.pop(
														    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
														__UpperCAmelCase   :						Dict      							=					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   :						Tuple      							=					in_proj_weight_cross_attn[:2_5_6, :]
														__UpperCAmelCase   :						int      							=					in_proj_bias_cross_attn[:2_5_6]
														__UpperCAmelCase   :						Optional[Any]      							=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
														__UpperCAmelCase   :						List[Any]      							=					in_proj_bias_cross_attn[2_5_6:5_1_2]
														__UpperCAmelCase   :						Union[str, Any]      							=					in_proj_weight_cross_attn[-2_5_6:, :]
														__UpperCAmelCase   :						Tuple      							=					in_proj_bias_cross_attn[-2_5_6:]
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      List[Any]							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase							,__UpperCAmelCase   :						Tuple      							=					image.size
							__UpperCAmelCase   :						Optional[Any]      							=					max(_UpperCamelCase       ,			_UpperCamelCase							)
							__UpperCAmelCase   :						str      							=					8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
							__UpperCAmelCase   :						Dict      							=					target_max_size / current_max_size
							__UpperCAmelCase   :						str      							=					image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

							return resized_image
def      lowerCamelCase       (     _UpperCamelCase		:      Any							)		->       Tuple:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					F.to_tensor(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					F.normalize(_UpperCamelCase       ,			mean=[0.485, 0.456, 0.406]       ,			std=[0.229, 0.224, 0.225]							)
							return image
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      int							)		->       int:

							'''simple docstring'''



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

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

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

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

							__UpperCAmelCase   :						int      							=					model(_UpperCamelCase							)

							if "detection" in checkpoint_url:
														__UpperCAmelCase   :						List[str]      							=					(1, 1_5, 3)
														__UpperCAmelCase   :						Any      							=					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]]							)
														__UpperCAmelCase   :						Optional[int]      							=					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:
														__UpperCAmelCase   :						int      							=					(1, 1_2_5, 7)
														__UpperCAmelCase   :						Dict      							=					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]]							)
														__UpperCAmelCase   :						Tuple      							=					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]       ,			_UpperCamelCase       ,			atol=1E-4							)
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,			_UpperCamelCase       ,			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(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
														model.save_pretrained(_UpperCamelCase							)
														image_processor.save_pretrained(_UpperCamelCase							)

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


if __name__ == "__main__":
							UpperCAmelCase    :     List[str]   						=		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    :     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

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
