






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 :      Any										=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase :      int										=				[]
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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							Optional[int]					,  snake_case_ :							Tuple       ):
	snake_case__			:				Optional[int]       =  state_dict.pop(snake_case_       )
	snake_case__			:				List[Any]       =  val







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]       ):
	snake_case__			:				List[str]       =  OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case__			:				List[str]       =  key.replace("backbone.0.body"					,  "backbone.conv_encoder.model"       )
			snake_case__			:				Optional[int]       =  value
		else:
			snake_case__			:				List[str]       =  value

	return new_state_dict







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							List[Any]       ):
	snake_case__,						snake_case__			:				Tuple       =  image.size
	snake_case__			:				Optional[Any]       =  max(snake_case_					,  snake_case_       )
	snake_case__			:				Tuple       =  800 if "detection" in checkpoint_url else 1000
	snake_case__			:				int       =  target_max_size / current_max_size
	snake_case__			:				str       =  image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

	return resized_image







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple       ):
	snake_case__			:				Any       =  F.to_tensor(snake_case_       )
	snake_case__			:				Tuple       =  F.normalize(snake_case_					,  mean=[0.4_85, 0.4_56, 0.4_06]					,  std=[0.2_29, 0.2_24, 0.2_25]       )
	return image







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							List[str]					,  snake_case_ :							Dict       ):
	logger.info("Converting model..."       )

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

	snake_case__			:				Any       =  DetrImageProcessor(
	    format="coco_detection"					,  max_size=800 if "detection" in checkpoint_url else 1000       )
	snake_case__			:				Union[str, Any]       =  TableTransformerForObjectDetection(snake_case_       )
	model.load_state_dict(snake_case_       )
	model.eval()

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

	snake_case__			:				Union[str, Any]       =  model(snake_case_       )

	if "detection" in checkpoint_url:
		snake_case__			:				List[str]       =  (1, 15, 3)
		snake_case__			:				Union[str, Any]       =  torch.tensor(
		    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]       )
		snake_case__			:				Optional[Any]       =  torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]       )

	else:
		snake_case__			:				int       =  (1, 125, 7)
		snake_case__			:				List[Any]       =  torch.tensor(
		    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]       )
		snake_case__			:				Dict       =  torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]       )

	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..."       )
		snake_case__			:				Dict       =  (
		    "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 :      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."""
	)
	__lowerCamelCase :      Tuple										=				parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











__lowerCamelCase :      Optional[int]										=				"""Tobias Carryer"""

from time import time



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  Dict=int(time()	)	):  # noqa: B008
						snake_case__			:				List[Any]       =  multiplier
						snake_case__			:				Optional[int]       =  increment
						snake_case__			:				Optional[int]       =  modulo
						snake_case__			:				Union[str, Any]       =  seed






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Union[str, Any]       =  (self.multiplier * self.seed + self.increment) % self.modulo
						return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	__lowerCamelCase :      int										=				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
	while True:
		print(lcg.next_number())



