




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	:       List[str]     =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase	:       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     __magic_name__       (				A	:      Dict,						A	:      List[str],						A	:      List[Any]     ):






			'''simple docstring'''







			a					=    state_dict.pop(A     )
			a					=    val




def     __magic_name__       (				A	:      List[Any]     ):






			'''simple docstring'''







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

			return new_state_dict




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    ""

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




def     __magic_name__       (				A	:      List[Any],						A	:      Optional[int]     ):






			'''simple docstring'''







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

			return resized_image




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			a					=    F.to_tensor(A     )
			a					=    F.normalize(A,						mean=[0.4_85, 0.4_56, 0.4_06],						std=[0.2_29, 0.2_24, 0.2_25]     )
			return image




@torch.no_grad()
def     __magic_name__       (				A	:      Tuple,						A	:      Dict,						A	:      int     ):






			'''simple docstring'''







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

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

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

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

			a					=    model(A     )

			if "detection" in checkpoint_url:
						a					=    (1, 15, 3)
						a					=    torch.tensor(
						    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]     )
						a					=    torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]     )

			else:
						a					=    (1, 125, 7)
						a					=    torch.tensor(
						    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]     )
						a					=    torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]     )

			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..."     )
						a					=    (
						    "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__":
		__lowerCAmelCase	:       Optional[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.'
		)
		__lowerCAmelCase	:       Dict     =	parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






