
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()
__magic_name__            =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__            =  []
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__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				state_dict.pop(_lowercase							)
    snake_case__						=				val





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

    return new_state_dict





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				image.size
    snake_case__						=				max(_lowercase    ,		_lowercase							)
    snake_case__						=				800 if '''detection''' in checkpoint_url else 1_000
    snake_case__						=				target_max_size / current_max_size
    snake_case__						=				image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

    return resized_image





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				F.to_tensor(_lowercase							)
    snake_case__						=				F.normalize(_lowercase    ,		mean=[0.485, 0.456, 0.406]    ,		std=[0.229, 0.224, 0.225]							)
    return image





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    logger.info("Converting model..."							)

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

    snake_case__						=				DetrImageProcessor(
        format="coco_detection"    ,		max_size=800 if "detection" in checkpoint_url else 1_000							)
    snake_case__						=				TableTransformerForObjectDetection(_lowercase							)
    model.load_state_dict(_lowercase							)
    model.eval()

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

    snake_case__						=				model(_lowercase							)

    if "detection" in checkpoint_url:
        snake_case__						=				(1, 15, 3)
        snake_case__						=				torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]							)
        snake_case__						=				torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]							)

    else:
        snake_case__						=				(1, 125, 7)
        snake_case__						=				torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]							)
        snake_case__						=				torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]							)

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]    ,		_lowercase    ,		atol=1E-4							)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,		_lowercase    ,		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(_lowercase							).mkdir(exist_ok=_lowercase							)
        model.save_pretrained(_lowercase							)
        image_processor.save_pretrained(_lowercase							)

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


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





from __future__ import annotations

from fractions import Fraction





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[]
    snake_case__						=				11
    snake_case__						=				int("1" + "0" * digit_len							)
    for num in range(__lowerCAmelCase    ,		__lowerCAmelCase							):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__lowerCAmelCase    ,		__lowerCAmelCase							):
                    solutions.append(F"""{num}/{den}"""							)
            den += 1
        num += 1
        snake_case__						=				10
    return solutions





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = 2							):
    snake_case__						=				1.0
    for fraction in fraction_list(__lowerCAmelCase							):
        snake_case__						=				Fraction(__lowerCAmelCase							)
        result *= frac.denominator / frac.numerator
    return int(__lowerCAmelCase							)


if __name__ == "__main__":
     print(solution())



