

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()
__A     :					List[Any]     = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A     :					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 :				str	,   _A :				Dict	,   _A :				Any						)							->Tuple:



							"""simple docstring"""
							lowerCamelCase_				     =state_dict.pop(UpperCAmelCase__						)
							lowerCamelCase_				     =val




def     __UpperCamelCase			(	_A :				List[str]						)							->Union[str, Any]:



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

							return new_state_dict




def     __UpperCamelCase			(	_A :				int						)							->Tuple:



							"""simple docstring"""
							lowerCamelCase_				     =""""""

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




def     __UpperCamelCase			(	_A :				int	,   _A :				Optional[int]						)							->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_				     =image.size
							lowerCamelCase_				     =max(UpperCAmelCase__	,   UpperCAmelCase__						)
							lowerCamelCase_				     =800 if """detection""" in checkpoint_url else 1000
							lowerCamelCase_				     =target_max_size / current_max_size
							lowerCamelCase_				     =image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

							return resized_image




def     __UpperCamelCase			(	_A :				List[Any]						)							->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_				     =F.to_tensor(UpperCAmelCase__						)
							lowerCamelCase_				     =F.normalize(UpperCAmelCase__	,   mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,   std=[0.2_2_9, 0.2_2_4, 0.2_2_5]						)
							return image




@torch.no_grad()
def     __UpperCamelCase			(	_A :				Dict	,   _A :				List[str]	,   _A :				Optional[Any]						)							->Union[str, Any]:



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

							# load original state dict
							lowerCamelCase_				     =torch.hub.load_state_dict_from_url(UpperCAmelCase__	,   map_location="""cpu"""						)
							# rename keys
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__						)
							lowerCamelCase_				     =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
							lowerCamelCase_				     ="""model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
																					lowerCamelCase_				     =state_dict.pop(UpperCAmelCase__						)
																					lowerCamelCase_				     =val
    # create HuggingFace model and load state dict
							lowerCamelCase_				     =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:
														lowerCamelCase_				     =15
														lowerCamelCase_				     =2
														lowerCamelCase_				     ={0: """table""", 1: """table rotated"""}
														lowerCamelCase_				     =idalabel
														lowerCamelCase_				     ={v: k for k, v in idalabel.items()}
							else:
														lowerCamelCase_				     =125
														lowerCamelCase_				     =6
														lowerCamelCase_				     ={
														    0: """table""",
														    1: """table column""",
														    2: """table row""",
														    3: """table column header""",
														    4: """table projected row header""",
														    5: """table spanning cell""",
														}
														lowerCamelCase_				     =idalabel
														lowerCamelCase_				     ={v: k for k, v in idalabel.items()}

							lowerCamelCase_				     =DetrImageProcessor(
							    format="""coco_detection"""	,   max_size=800 if """detection""" in checkpoint_url else 1000						)
							lowerCamelCase_				     =TableTransformerForObjectDetection(UpperCAmelCase__						)
							model.load_state_dict(UpperCAmelCase__						)
							model.eval()

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

							lowerCamelCase_				     =model(UpperCAmelCase__						)

							if "detection" in checkpoint_url:
														lowerCamelCase_				     =(1, 15, 3)
														lowerCamelCase_				     =torch.tensor(
														    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]						)
														lowerCamelCase_				     =torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]						)

							else:
														lowerCamelCase_				     =(1, 125, 7)
														lowerCamelCase_				     =torch.tensor(
														    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]						)
														lowerCamelCase_				     =torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]						)

							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..."""						)
														lowerCamelCase_				     =(
														    """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__":
	__A     :					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.'
	)
	__A     :					Dict     = parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1      ,      UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i - 1][j] + matrix[i][j] )

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i + 1][j] + matrix[i][j] )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )


if __name__ == "__main__":
						print(f"""{solution() = }""")





