






"""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         =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase         =							[]
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	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   state_dict.pop(snake_case__					)
	A_		:						str	    =   val






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			A_		:						str	    =   key.replace("""backbone.0.body"""   ,   """backbone.conv_encoder.model"""					)
			A_		:						Dict	    =   value
		else:
			A_		:						Any	    =   value

	return new_state_dict






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   """"""

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_    ,     A_		:						Optional[Any]	    =   image.size
	A_		:						Tuple	    =   max(snake_case__   ,   snake_case__					)
	A_		:						Union[str, Any]	    =   800 if """detection""" in checkpoint_url else 1_000
	A_		:						Dict	    =   target_max_size / current_max_size
	A_		:						str	    =   image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

	return resized_image






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Any	    =   F.to_tensor(snake_case__					)
	A_		:						Union[str, Any]	    =   F.normalize(snake_case__   ,   mean=[0.485, 0.456, 0.406]   ,   std=[0.229, 0.224, 0.225]					)
	return image






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	logger.info("""Converting model..."""					)

	# load original state dict
	A_		:						Union[str, Any]	    =   torch.hub.load_state_dict_from_url(snake_case__   ,   map_location="""cpu"""					)
	# rename keys
	for src, dest in rename_keys:
		rename_key(snake_case__   ,   snake_case__   ,   snake_case__					)
	A_		:						Union[str, Any]	    =   rename_backbone_keys(snake_case__					)
	# query, key and value matrices need special treatment
	read_in_q_k_v(snake_case__					)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	A_		:						Optional[Any]	    =   """model."""
	for key in state_dict.copy().keys():
		if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
			A_		:						Tuple	    =   state_dict.pop(snake_case__					)
			A_		:						List[str]	    =   val
    # create HuggingFace model and load state dict
	A_		:						List[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:
		A_		:						Optional[Any]	    =   15
		A_		:						Optional[int]	    =   2
		A_		:						int	    =   {0: """table""", 1: """table rotated"""}
		A_		:						Union[str, Any]	    =   idalabel
		A_		:						Optional[Any]	    =   {v: k for k, v in idalabel.items()}
	else:
		A_		:						Optional[int]	    =   125
		A_		:						List[Any]	    =   6
		A_		:						int	    =   {
		    0: """table""",
		    1: """table column""",
		    2: """table row""",
		    3: """table column header""",
		    4: """table projected row header""",
		    5: """table spanning cell""",
		}
		A_		:						Optional[Any]	    =   idalabel
		A_		:						Optional[Any]	    =   {v: k for k, v in idalabel.items()}

	A_		:						List[str]	    =   DetrImageProcessor(
	    format="""coco_detection"""   ,   max_size=800 if """detection""" in checkpoint_url else 1_000					)
	A_		:						Optional[int]	    =   TableTransformerForObjectDetection(snake_case__					)
	model.load_state_dict(snake_case__					)
	model.eval()

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

	A_		:						Optional[Any]	    =   model(snake_case__					)

	if "detection" in checkpoint_url:
		A_		:						List[str]	    =   (1, 15, 3)
		A_		:						str	    =   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]]					)
		A_		:						Tuple	    =   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:
		A_		:						str	    =   (1, 125, 7)
		A_		:						Optional[Any]	    =   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]]					)
		A_		:						int	    =   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]   ,   snake_case__   ,   atol=1E-4					)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,   snake_case__   ,   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(snake_case__					).mkdir(exist_ok=snake_case__					)
		model.save_pretrained(snake_case__					)
		image_processor.save_pretrained(snake_case__					)

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


if __name__ == "__main__":
							_lowerCAmelCase         =							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         =							parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)

