


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()
snake_case      :   Tuple							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case      :   Optional[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 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			state_dict.pop(UpperCAmelCase__				)
				_SCREAMING_SNAKE_CASE   =			val


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_SCREAMING_SNAKE_CASE   =			key.replace('backbone.0.body' ,'backbone.conv_encoder.model'				)
												_SCREAMING_SNAKE_CASE   =			value
								else:
												_SCREAMING_SNAKE_CASE   =			value

				return new_state_dict


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			''''''

				# 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)
								_SCREAMING_SNAKE_CASE   =			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
								_SCREAMING_SNAKE_CASE   =			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
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   =			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
								_SCREAMING_SNAKE_CASE   =			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
								_SCREAMING_SNAKE_CASE   =			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
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								_SCREAMING_SNAKE_CASE   =			state_dict.pop(
								    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
								_SCREAMING_SNAKE_CASE   =			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
								_SCREAMING_SNAKE_CASE   =			in_proj_weight_cross_attn[:256, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE   =			in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE   =			in_proj_bias_cross_attn[-256:]


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__ ,UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			image.size
				_SCREAMING_SNAKE_CASE   =			max(UpperCAmelCase__ ,UpperCAmelCase__				)
				_SCREAMING_SNAKE_CASE   =			800 if '''detection''' in checkpoint_url else 1000
				_SCREAMING_SNAKE_CASE   =			target_max_size / current_max_size
				_SCREAMING_SNAKE_CASE   =			image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

				return resized_image


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			F.to_tensor(UpperCAmelCase__				)
				_SCREAMING_SNAKE_CASE   =			F.normalize(UpperCAmelCase__ ,mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] ,std=[0.2_2_9, 0.2_2_4, 0.2_2_5]				)
				return image


@torch.no_grad()
def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__				):

				"""simple docstring"""






				logger.info('Converting model...'				)

				# load original state dict
				_SCREAMING_SNAKE_CASE   =			torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,map_location='cpu'				)
				# rename keys
				for src, dest in rename_keys:
								rename_key(UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__				)
				_SCREAMING_SNAKE_CASE   =			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
				_SCREAMING_SNAKE_CASE   =			'''model.'''
				for key in state_dict.copy().keys():
								if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
												_SCREAMING_SNAKE_CASE   =			state_dict.pop(UpperCAmelCase__				)
												_SCREAMING_SNAKE_CASE   =			val
    # create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE   =			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:
								_SCREAMING_SNAKE_CASE   =			15
								_SCREAMING_SNAKE_CASE   =			2
								_SCREAMING_SNAKE_CASE   =			{0: '''table''', 1: '''table rotated'''}
								_SCREAMING_SNAKE_CASE   =			idalabel
								_SCREAMING_SNAKE_CASE   =			{v: k for k, v in idalabel.items()}
				else:
								_SCREAMING_SNAKE_CASE   =			125
								_SCREAMING_SNAKE_CASE   =			6
								_SCREAMING_SNAKE_CASE   =			{
								    0: '''table''',
								    1: '''table column''',
								    2: '''table row''',
								    3: '''table column header''',
								    4: '''table projected row header''',
								    5: '''table spanning cell''',
								}
								_SCREAMING_SNAKE_CASE   =			idalabel
								_SCREAMING_SNAKE_CASE   =			{v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE   =			DetrImageProcessor(
				    format='coco_detection' ,max_size=800 if 'detection' in checkpoint_url else 1000				)
				_SCREAMING_SNAKE_CASE   =			TableTransformerForObjectDetection(UpperCAmelCase__				)
				model.load_state_dict(UpperCAmelCase__				)
				model.eval()

				# verify our conversion
				_SCREAMING_SNAKE_CASE   =			'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
				_SCREAMING_SNAKE_CASE   =			hf_hub_download(repo_id='nielsr/example-pdf' ,repo_type='dataset' ,filename=UpperCAmelCase__				)
				_SCREAMING_SNAKE_CASE   =			Image.open(UpperCAmelCase__				).convert('RGB'				)
				_SCREAMING_SNAKE_CASE   =			normalize(resize(UpperCAmelCase__ ,UpperCAmelCase__				)				).unsqueeze(0				)

				_SCREAMING_SNAKE_CASE   =			model(UpperCAmelCase__				)

				if "detection" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   =			(1, 15, 3)
								_SCREAMING_SNAKE_CASE   =			torch.tensor(
								    [[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]]				)
								_SCREAMING_SNAKE_CASE   =			torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]				)

				else:
								_SCREAMING_SNAKE_CASE   =			(1, 125, 7)
								_SCREAMING_SNAKE_CASE   =			torch.tensor(
								    [[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]]				)
								_SCREAMING_SNAKE_CASE   =			torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]				)

				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...'				)
								_SCREAMING_SNAKE_CASE   =			(
								    '''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__":
							snake_case      :   Union[str, 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.'
							)
							snake_case      :   Tuple							= parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''





import math

import flax.linen as nn
import jax.numpy as jnp






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

						if flip_sin_to_cos:
												lowercase :							Dict    		=jnp.concatenate([jnp.cos(__magic_name__						), jnp.sin(__magic_name__						)]   , axis=1						)
						else:
												lowercase :							Any    		=jnp.concatenate([jnp.sin(__magic_name__						), jnp.cos(__magic_name__						)]   , axis=1						)
						lowercase :							List[str]    		=jnp.reshape(__magic_name__   , [jnp.shape(__magic_name__						)[0], embedding_dim]						)
						return signal





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





