






'''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()
UpperCamelCase     :   Any	      =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase     :   Tuple	      =		[]
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__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      str    ):
						lowerCamelCase__								=	state_dict.pop(__lowerCAmelCase    )
						lowerCamelCase__								=	val
def     A__      (					__lowerCAmelCase		:      Optional[int]    ):
						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     A__      (					__lowerCAmelCase		:      Any    ):
						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     A__      (					__lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Dict    ):
						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     A__      (					__lowerCAmelCase		:      Any    ):
						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     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      List[Any]    ):
						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__":
							UpperCamelCase     :   List[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.'
							)
							UpperCamelCase     :   str	      =		parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'biogpt'







	def __init__(     self      :Tuple   ,     lowercase      :Union[str, Any]=4_2_3_8_4   ,     lowercase      :str=1_0_2_4   ,     lowercase      :Any=2_4   ,     lowercase      :List[str]=1_6   ,     lowercase      :List[str]=4_0_9_6   ,     lowercase      :int="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :int=0.1   ,     lowercase      :int=1_0_2_4   ,     lowercase      :Optional[Any]=0.02   ,     lowercase      :Union[str, Any]=1e-12   ,     lowercase      :Tuple=True   ,     lowercase      :int=True   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Any=0.0   ,     lowercase      :str=1   ,     lowercase      :Any=0   ,     lowercase      :int=2   ,     **lowercase      :Tuple   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= scale_embedding
				SCREAMING_SNAKE_CASE     						= use_cache
				SCREAMING_SNAKE_CASE     						= layerdrop
				SCREAMING_SNAKE_CASE     						= activation_dropout
				super().__init__(pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     **lowercase			)