






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_							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_							=    []
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  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				str   ) ->   str:
						"""simple docstring"""







						lowerCAmelCase__       =						state_dict.pop(UpperCamelCase_   )
						lowerCAmelCase__       =						val







def        _a  (     UpperCamelCase_					:				Tuple   ) ->   str:
						"""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        _a  (     UpperCamelCase_					:				List[Any]   ) ->   int:
						"""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        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Optional[int]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						image.size
						lowerCAmelCase__       =						max(UpperCamelCase_    ,   UpperCamelCase_   )
						lowerCAmelCase__       =						800 if "detection" in checkpoint_url else 1_000
						lowerCAmelCase__       =						target_max_size / current_max_size
						lowerCAmelCase__       =						image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

						return resized_image







def        _a  (     UpperCamelCase_					:				List[Any]   ) ->   Tuple:
						"""simple docstring"""







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







@torch.no_grad()
def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				List[Any]   ) ->   Tuple:
						"""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 1_000   )
						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_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]]   )
												lowerCAmelCase__       =						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:
												lowerCAmelCase__       =						(1, 125, 7)
												lowerCAmelCase__       =						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]]   )
												lowerCAmelCase__       =						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..."   )
												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_							=    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_							=    parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)














from __future__ import annotations

from collections.abc import Generator


def        _a  (     ) ->   Generator[int, None, None]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						2
						while True:
												lowerCAmelCase__       =						factor_map.pop(UpperCamelCase_    ,   UpperCamelCase_   )
												if factor:
																		lowerCAmelCase__       =						factor + prime
																		while x in factor_map:
																								x += factor
																		lowerCAmelCase__       =						factor
												else:
																		lowerCAmelCase__       =						prime
																		yield prime
												prime += 1


def        _a  (     UpperCamelCase_					:				float = 1e1_0   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						sieve()

						lowerCAmelCase__       =						1
						while True:
												lowerCAmelCase__       =						next(UpperCamelCase_   )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(UpperCamelCase_   )
												n += 2


if __name__ == "__main__":
	print(solution())






