

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  :      List[str]			 =logging.get_logger(__name__)

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



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

	return new_state_dict



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	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)
		UpperCAmelCase__:	Union[str, Any]									= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
		UpperCAmelCase__:	Optional[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
		UpperCAmelCase__:	Tuple									= in_proj_weight[:2_5_6, :]
		UpperCAmelCase__:	str									= in_proj_bias[:2_5_6]
		UpperCAmelCase__:	Optional[int]									= in_proj_weight[2_5_6:5_1_2, :]
		UpperCAmelCase__:	List[Any]									= in_proj_bias[2_5_6:5_1_2]
		UpperCAmelCase__:	Any									= in_proj_weight[-2_5_6:, :]
		UpperCAmelCase__:	List[str]									= in_proj_bias[-2_5_6:]
	# 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
		UpperCAmelCase__:	Tuple									= state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
		UpperCAmelCase__:	Any									= 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
		UpperCAmelCase__:	List[Any]									= in_proj_weight[:2_5_6, :]
		UpperCAmelCase__:	Optional[int]									= in_proj_bias[:2_5_6]
		UpperCAmelCase__:	Optional[int]									= in_proj_weight[2_5_6:5_1_2, :]
		UpperCAmelCase__:	Optional[int]									= in_proj_bias[2_5_6:5_1_2]
		UpperCAmelCase__:	Optional[Any]									= in_proj_weight[-2_5_6:, :]
		UpperCAmelCase__:	Optional[int]									= in_proj_bias[-2_5_6:]
		# read in weights + bias of input projection layer of cross-attention
		UpperCAmelCase__:	Tuple									= state_dict.pop(
		    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"      )
		UpperCAmelCase__:	List[str]									= 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
		UpperCAmelCase__:	List[Any]									= in_proj_weight_cross_attn[:2_5_6, :]
		UpperCAmelCase__:	Optional[Any]									= in_proj_bias_cross_attn[:2_5_6]
		UpperCAmelCase__:	str									= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		UpperCAmelCase__:	Union[str, Any]									= in_proj_bias_cross_attn[2_5_6:5_1_2]
		UpperCAmelCase__:	Optional[int]									= in_proj_weight_cross_attn[-2_5_6:, :]
		UpperCAmelCase__:	Optional[int]									= in_proj_bias_cross_attn[-2_5_6:]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	Optional[int]									= image.size
	UpperCAmelCase__:	Tuple									= max(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[str]									= 8_0_0 if "detection" in checkpoint_url else 1_0_0_0
	UpperCAmelCase__:	Optional[int]									= target_max_size / current_max_size
	UpperCAmelCase__:	Dict									= image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

	return resized_image



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Any									= F.to_tensor(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= F.normalize(SCREAMING_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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	logger.info("Converting model..."      )

	# load original state dict
	UpperCAmelCase__:	Union[str, Any]									= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE	,map_location="cpu"      )
	# rename keys
	for src, dest in rename_keys:
		rename_key(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	int									= rename_backbone_keys(SCREAMING_SNAKE_CASE      )
	# query, key and value matrices need special treatment
	read_in_q_k_v(SCREAMING_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
	UpperCAmelCase__:	List[Any]									= "model."
	for key in state_dict.copy().keys():
		if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
			UpperCAmelCase__:	Optional[Any]									= state_dict.pop(SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Any									= val
    # create HuggingFace model and load state dict
	UpperCAmelCase__:	Optional[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:
		UpperCAmelCase__:	Optional[Any]									= 1_5
		UpperCAmelCase__:	Any									= 2
		UpperCAmelCase__:	List[Any]									= {0: "table", 1: "table rotated"}
		UpperCAmelCase__:	int									= idalabel
		UpperCAmelCase__:	Optional[int]									= {v: k for k, v in idalabel.items()}
	else:
		UpperCAmelCase__:	Union[str, Any]									= 1_2_5
		UpperCAmelCase__:	Union[str, Any]									= 6
		UpperCAmelCase__:	Dict									= {
		    0: "table",
		    1: "table column",
		    2: "table row",
		    3: "table column header",
		    4: "table projected row header",
		    5: "table spanning cell",
		}
		UpperCAmelCase__:	int									= idalabel
		UpperCAmelCase__:	str									= {v: k for k, v in idalabel.items()}

	UpperCAmelCase__:	int									= DetrImageProcessor(
	    format="coco_detection"	,max_size=8_0_0 if "detection" in checkpoint_url else 1_0_0_0      )
	UpperCAmelCase__:	List[str]									= TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE      )
	model.load_state_dict(SCREAMING_SNAKE_CASE      )
	model.eval()

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

	UpperCAmelCase__:	Optional[Any]									= model(SCREAMING_SNAKE_CASE      )

	if "detection" in checkpoint_url:
		UpperCAmelCase__:	Dict									= (1, 1_5, 3)
		UpperCAmelCase__:	Tuple									= 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]]      )
		UpperCAmelCase__:	List[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:
		UpperCAmelCase__:	List[Any]									= (1, 1_2_5, 7)
		UpperCAmelCase__:	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]]      )
		UpperCAmelCase__:	Optional[Any]									= 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]	,SCREAMING_SNAKE_CASE	,atol=1e-4      )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,SCREAMING_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(SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
		model.save_pretrained(SCREAMING_SNAKE_CASE      )
		image_processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	if push_to_hub:
		# Push model to HF hub
		logger.info("Pushing model to the hub..."      )
		UpperCAmelCase__:	Optional[int]									= (
		    "microsoft/table-transformer-detection"
		    if "detection" in checkpoint_url
		    else "microsoft/table-transformer-structure-recognition"
		)
		model.push_to_hub(SCREAMING_SNAKE_CASE      )
		image_processor.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					_lowerCAmelCase  :      Tuple			 =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  :      Union[str, Any]			 =parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()