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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase						: 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__       :					str      ,      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					Any )			->      Tuple:
							lowercase_					:			int										=       state_dict.pop(UpperCAmelCase__ )
							lowercase_					:			Optional[Any]										=       val




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[str] )			->      Union[str, Any]:
							lowercase_					:			List[Any]										=       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowercase_					:			Dict										=       key.replace("""backbone.0.body"""      ,      """backbone.conv_encoder.model""" )
																					lowercase_					:			Any										=       value
														else:
																					lowercase_					:			Optional[Any]										=       value

							return new_state_dict




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      Tuple:
							lowercase_					:			Union[str, 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)
														lowercase_					:			int										=       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
														lowercase_					:			Tuple										=       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
														lowercase_					:			Dict										=       in_proj_weight[:256, :]
														lowercase_					:			Tuple										=       in_proj_bias[:256]
														lowercase_					:			Optional[Any]										=       in_proj_weight[256:512, :]
														lowercase_					:			Tuple										=       in_proj_bias[256:512]
														lowercase_					:			Optional[Any]										=       in_proj_weight[-256:, :]
														lowercase_					:			Optional[int]										=       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
														lowercase_					:			Optional[int]										=       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
														lowercase_					:			Optional[int]										=       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
														lowercase_					:			Union[str, Any]										=       in_proj_weight[:256, :]
														lowercase_					:			Optional[Any]										=       in_proj_bias[:256]
														lowercase_					:			Optional[Any]										=       in_proj_weight[256:512, :]
														lowercase_					:			Dict										=       in_proj_bias[256:512]
														lowercase_					:			Optional[Any]										=       in_proj_weight[-256:, :]
														lowercase_					:			Optional[Any]										=       in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														lowercase_					:			str										=       state_dict.pop(
														    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
														lowercase_					:			List[Any]										=       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
														lowercase_					:			List[str]										=       in_proj_weight_cross_attn[:256, :]
														lowercase_					:			Union[str, Any]										=       in_proj_bias_cross_attn[:256]
														lowercase_					:			Dict										=       in_proj_weight_cross_attn[256:512, :]
														lowercase_					:			Union[str, Any]										=       in_proj_bias_cross_attn[256:512]
														lowercase_					:			Tuple										=       in_proj_weight_cross_attn[-256:, :]
														lowercase_					:			List[str]										=       in_proj_bias_cross_attn[-256:]




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					Optional[int] )			->      Union[str, Any]:
							lowercase_						,						lowercase_					:			Union[str, Any]										=       image.size
							lowercase_					:			Optional[int]										=       max(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       800 if """detection""" in checkpoint_url else 1000
							lowercase_					:			Any										=       target_max_size / current_max_size
							lowercase_					:			int										=       image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

							return resized_image




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[Any] )			->      Union[str, Any]:
							lowercase_					:			Dict										=       F.to_tensor(UpperCAmelCase__ )
							lowercase_					:			int										=       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__       :					Dict      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					Optional[Any] )			->      Union[str, Any]:
							logger.info("""Converting model...""" )

							# load original state dict
							lowercase_					:			Optional[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__ )
							lowercase_					:			Optional[int]										=       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
							lowercase_					:			Tuple										=       """model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																					lowercase_					:			int										=       state_dict.pop(UpperCAmelCase__ )
																					lowercase_					:			List[Any]										=       val
    # create HuggingFace model and load state dict
							lowercase_					:			List[str]										=       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:
														lowercase_					:			Optional[int]										=       15
														lowercase_					:			Optional[Any]										=       2
														lowercase_					:			List[Any]										=       {0: """table""", 1: """table rotated"""}
														lowercase_					:			Tuple										=       idalabel
														lowercase_					:			Optional[Any]										=       {v: k for k, v in idalabel.items()}
							else:
														lowercase_					:			Any										=       125
														lowercase_					:			Optional[int]										=       6
														lowercase_					:			List[Any]										=       {
														    0: """table""",
														    1: """table column""",
														    2: """table row""",
														    3: """table column header""",
														    4: """table projected row header""",
														    5: """table spanning cell""",
														}
														lowercase_					:			Optional[Any]										=       idalabel
														lowercase_					:			Optional[Any]										=       {v: k for k, v in idalabel.items()}

							lowercase_					:			Optional[int]										=       DetrImageProcessor(
							    format="""coco_detection"""      ,      max_size=800 if """detection""" in checkpoint_url else 1000 )
							lowercase_					:			Any										=       TableTransformerForObjectDetection(UpperCAmelCase__ )
							model.load_state_dict(UpperCAmelCase__ )
							model.eval()

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

							lowercase_					:			Union[str, Any]										=       model(UpperCAmelCase__ )

							if "detection" in checkpoint_url:
														lowercase_					:			List[Any]										=       (1, 15, 3)
														lowercase_					:			Any										=       torch.tensor(
														    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
														lowercase_					:			str										=       torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )

							else:
														lowercase_					:			Dict										=       (1, 125, 7)
														lowercase_					:			Union[str, Any]										=       torch.tensor(
														    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
														lowercase_					:			Union[str, Any]										=       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]      ,      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...""" )
														lowercase_					:			Dict										=       (
														    """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__":
						_lowercase						: 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."
						)
						_lowercase						: Dict						=						parser.parse_args()
						convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import math




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      int:

							if not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ):
														lowercase_					:			List[Any]										=       F'''Input value of [number={number}] must be an integer'''
														raise TypeError(UpperCAmelCase__ )

							if number < 1:
														lowercase_					:			Tuple										=       F'''Input value of [number={number}] must be > 0'''
														raise ValueError(UpperCAmelCase__ )
							elif number == 1:
														return 3
							elif number == 2:
														return 5
							else:
														lowercase_					:			Union[str, Any]										=       int(math.log(number // 3      ,      2 ) ) + 2

														lowercase_					:			Any										=       [3, 5]
														lowercase_					:			List[Any]										=       2
														lowercase_					:			Optional[Any]										=       3
														for block in range(1      ,      UpperCAmelCase__ ):
																					for _ in range(UpperCAmelCase__ ):
																												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
																												proth_index += 1
																					increment *= 2

							return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												_lowercase						: Optional[Any]						=						0
												try:
																		_lowercase						: str						=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")





