



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_													=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_													=      []
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		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     List[Any]						)	->    Union[str, Any]:
					__lowerCAmelCase						 =state_dict.pop(__lowerCamelCase						)
					__lowerCAmelCase						 =val






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple						)	->    Dict:
					__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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]						)	->    Dict:
					__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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Optional[Any]						)	->    str:
					__lowerCAmelCase ,						__lowerCAmelCase						 =image.size
					__lowerCAmelCase						 =max(__lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =800 if """detection""" in checkpoint_url else 1000
					__lowerCAmelCase						 =target_max_size / current_max_size
					__lowerCAmelCase						 =image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

					return resized_image






def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]						)	->    Optional[int]:
					__lowerCAmelCase						 =F.to_tensor(__lowerCamelCase						)
					__lowerCAmelCase						 =F.normalize(__lowerCamelCase					, mean=[0.485, 0.456, 0.406]					, std=[0.229, 0.224, 0.225]						)
					return image






@torch.no_grad()
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     Dict						)	->    Optional[int]:
					logger.info("""Converting model..."""						)

					# load original state dict
					__lowerCAmelCase						 =torch.hub.load_state_dict_from_url(__lowerCamelCase					, map_location="""cpu"""						)
					# rename keys
					for src, dest in rename_keys:
										rename_key(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =rename_backbone_keys(__lowerCamelCase						)
					# query, key and value matrices need special treatment
					read_in_q_k_v(__lowerCamelCase						)
					# 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(__lowerCamelCase						)
															__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 1000						)
					__lowerCAmelCase						 =TableTransformerForObjectDetection(__lowerCamelCase						)
					model.load_state_dict(__lowerCamelCase						)
					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=__lowerCamelCase						)
					__lowerCAmelCase						 =Image.open(__lowerCamelCase						).convert("""RGB"""						)
					__lowerCAmelCase						 =normalize(resize(__lowerCamelCase					, __lowerCamelCase						)						).unsqueeze(0						)

					__lowerCAmelCase						 =model(__lowerCamelCase						)

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

					else:
										__lowerCAmelCase						 =(1, 125, 7)
										__lowerCAmelCase						 =torch.tensor(
										    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]						)
										__lowerCAmelCase						 =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]					, __lowerCamelCase					, atol=1E-4						)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]					, __lowerCamelCase					, 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(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
										model.save_pretrained(__lowerCamelCase						)
										image_processor.save_pretrained(__lowerCamelCase						)

					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(__lowerCamelCase						)
										image_processor.push_to_hub(__lowerCamelCase						)


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









from manim import *






class     __a			(  SCREAMING_SNAKE_CASE					):



	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__lowerCAmelCase						 =Rectangle(height=0.5					,						width=0.5)
						__lowerCAmelCase						 =Rectangle(height=0.4_6					,						width=0.4_6).set_stroke(width=0)

						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""CPU"""					,						font_size=24)
						__lowerCAmelCase						 =Group(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0.5					,						aligned_edge=snake_case_)
						cpu.move_to([-2.5, -0.5, 0])
						self.add(snake_case_)

						__lowerCAmelCase						 =[mem.copy() for i in range(1)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""GPU"""					,						font_size=24)
						__lowerCAmelCase						 =Group(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0.5					,						aligned_edge=snake_case_)
						gpu.align_to(snake_case_					,						snake_case_)
						gpu.set_x(gpu.get_x() - 1)

						self.add(snake_case_)

						__lowerCAmelCase						 =[mem.copy() for i in range(6)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)

						__lowerCAmelCase						 =Text("""Model"""					,						font_size=24)
						__lowerCAmelCase						 =Group(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0.5					,						aligned_edge=snake_case_)
						model.move_to([3, -1.0, 0])

						self.play(
						    Create(snake_case_					,						run_time=1)					,						Create(snake_case_					,						run_time=1)					,						Create(snake_case_					,						run_time=1)					,						)

						__lowerCAmelCase						 =MarkupText(
						    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""					,						font_size=24					,						)

						__lowerCAmelCase						 =Square(side_length=2.2)
						key.move_to([-5, 2, 0])

						__lowerCAmelCase						 =MarkupText(
						    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""					,						font_size=18					,						)

						key_text.move_to([-5, 2.4, 0])

						step_a.move_to([2, 2, 0])
						self.play(Write(snake_case_					,						run_time=2.5)					,						Write(snake_case_)					,						Write(snake_case_))

						self.add(snake_case_)

						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						for i, rect in enumerate(snake_case_):
											__lowerCAmelCase						 =Rectangle(height=0.4_6					,						width=0.4_6).set_stroke(width=0.0).set_fill(snake_case_					,						opacity=0.7)
											cpu_target.move_to(snake_case_)
											cpu_target.generate_target()
											__lowerCAmelCase						 =0.4_6 / 4
											__lowerCAmelCase						 =0.4_6 / 3

											if i == 0:
																cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)					,						buff=0.0_2					,						direction=snake_case_)
																cpu_target.target.set_x(cpu_target.target.get_x() + 0.1)
											elif i == 3:
																cpu_target.target.next_to(cpu_targs[0].target					,						direction=snake_case_					,						buff=0.0)
											else:
																cpu_target.target.next_to(cpu_targs[i - 1].target					,						direction=snake_case_					,						buff=0.0)
											cpu_targs.append(snake_case_)

											first_animations.append(rect.animate(run_time=0.5).set_stroke(snake_case_))
											second_animations.append(MoveToTarget(snake_case_					,						run_time=1.5))

						self.play(*snake_case_)
						self.play(*snake_case_)

						self.wait()




