





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__											= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__											= []
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     _UpperCamelCase     (a__   :List[Any]   ,			a__   :List[Any]   ,			a__   :Any       ):






							"""simple docstring"""





							UpperCamelCase__				     =			state_dict.pop(a__       )
							UpperCamelCase__				     =			val





def     _UpperCamelCase     (a__   :Dict       ):






							"""simple docstring"""





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

							return new_state_dict





def     _UpperCamelCase     (a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			""""""

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





def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :Optional[int]       ):






							"""simple docstring"""





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

							return resized_image





def     _UpperCamelCase     (a__   :List[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			F.to_tensor(a__       )
							UpperCamelCase__				     =			F.normalize(a__   ,			mean=[0.485, 0.456, 0.406]   ,			std=[0.229, 0.224, 0.225]       )
							return image





@torch.no_grad()
def     _UpperCamelCase     (a__   :Dict   ,			a__   :Dict   ,			a__   :int       ):






							"""simple docstring"""





							logger.info("""Converting model..."""       )

							# load original state dict
							UpperCamelCase__				     =			torch.hub.load_state_dict_from_url(a__   ,			map_location="""cpu"""       )
							# rename keys
							for src, dest in rename_keys:
														rename_key(a__   ,			a__   ,			a__       )
							UpperCamelCase__				     =			rename_backbone_keys(a__       )
							# query, key and value matrices need special treatment
							read_in_q_k_v(a__       )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCamelCase__				     =			"""model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
																					UpperCamelCase__				     =			state_dict.pop(a__       )
																					UpperCamelCase__				     =			val
    # create HuggingFace model and load state dict
							UpperCamelCase__				     =			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__				     =			15
														UpperCamelCase__				     =			2
														UpperCamelCase__				     =			{0: """table""", 1: """table rotated"""}
														UpperCamelCase__				     =			idalabel
														UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}
							else:
														UpperCamelCase__				     =			125
														UpperCamelCase__				     =			6
														UpperCamelCase__				     =			{
														    0: """table""",
														    1: """table column""",
														    2: """table row""",
														    3: """table column header""",
														    4: """table projected row header""",
														    5: """table spanning cell""",
														}
														UpperCamelCase__				     =			idalabel
														UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}

							UpperCamelCase__				     =			DetrImageProcessor(
							    format="""coco_detection"""   ,			max_size=800 if """detection""" in checkpoint_url else 1000       )
							UpperCamelCase__				     =			TableTransformerForObjectDetection(a__       )
							model.load_state_dict(a__       )
							model.eval()

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

							UpperCamelCase__				     =			model(a__       )

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

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

							if push_to_hub:
														# Push model to HF hub
														logger.info("""Pushing model to the hub..."""       )
														UpperCamelCase__				     =			(
														    """microsoft/table-transformer-detection"""
														    if """detection""" in checkpoint_url
														    else """microsoft/table-transformer-structure-recognition"""
														)
														model.push_to_hub(a__       )
														image_processor.push_to_hub(a__       )


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__											= {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= ["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= [
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					UpperCamelCase__											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




