
"""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()
lowerCamelCase__           =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__           =	[]
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       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   state_dict.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   val
def __lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__lowerCAmelCase    :		List[Any]		    =   key.replace('backbone.0.body' ,						'backbone.conv_encoder.model'	)
			__lowerCAmelCase    :		Dict		    =   value
		else:
			__lowerCAmelCase    :		Tuple		    =   value

	return new_state_dict
def __lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   ''

	# 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    :		List[Any]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Union[str, Any]		    =   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    :		List[str]		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Optional[Any]		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		Optional[Any]		    =   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    :		Union[str, Any]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Union[str, 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
		__lowerCAmelCase    :		str		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		Dict		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		__lowerCAmelCase    :		Any		    =   state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Union[str, 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
		__lowerCAmelCase    :		Optional[Any]		    =   in_proj_weight_cross_attn[:256, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias_cross_attn[:256]
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight_cross_attn[256:512, :]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias_cross_attn[256:512]
		__lowerCAmelCase    :		str		    =   in_proj_weight_cross_attn[-256:, :]
		__lowerCAmelCase    :		Dict		    =   in_proj_bias_cross_attn[-256:]
def __lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   image.size
	__lowerCAmelCase    :		Any		    =   max(_UpperCamelCase ,						_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   800 if 'detection' in checkpoint_url else 1000
	__lowerCAmelCase    :		List[str]		    =   target_max_size / current_max_size
	__lowerCAmelCase    :		Union[str, Any]		    =   image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

	return resized_image
def __lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Any		    =   F.to_tensor(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   F.normalize(_UpperCamelCase ,						mean=[0.485, 0.456, 0.406] ,						std=[0.229, 0.224, 0.225]	)
	return image
@torch.no_grad()
def __lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	logger.info('Converting model...'	)

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

	__lowerCAmelCase    :		Dict		    =   DetrImageProcessor(
	    format='coco_detection' ,						max_size=800 if 'detection' in checkpoint_url else 1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   TableTransformerForObjectDetection(_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)
	model.eval()

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

	__lowerCAmelCase    :		Optional[int]		    =   model(_UpperCamelCase	)

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

	else:
		__lowerCAmelCase    :		int		    =   (1, 125, 7)
		__lowerCAmelCase    :		Dict		    =   torch.tensor(
		    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]	)
		__lowerCAmelCase    :		Tuple		    =   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] ,						_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...'	)
		__lowerCAmelCase    :		List[Any]		    =   (
		    '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__":
							lowerCamelCase__           =	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__           =	parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip