

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()
__A			:					Dict						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A			:					List[str]						=			[]
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     (      A__			:  str			,  A__			:  Any			,  A__			:  Optional[Any]       ):
    SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
    SCREAMING_SNAKE_CASE										=   val





def 	__a     (      A__			:  Tuple       ):
    SCREAMING_SNAKE_CASE										=   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            SCREAMING_SNAKE_CASE										=   key.replace("backbone.0.body"			,  "backbone.conv_encoder.model"       )
            SCREAMING_SNAKE_CASE										=   value
        else:
            SCREAMING_SNAKE_CASE										=   value

    return new_state_dict





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





def 	__a     (      A__			:  Tuple			,  A__			:  int       ):
    SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   image.size
    SCREAMING_SNAKE_CASE										=   max(A__			,  A__       )
    SCREAMING_SNAKE_CASE										=   800 if "detection" in checkpoint_url else 1000
    SCREAMING_SNAKE_CASE										=   target_max_size / current_max_size
    SCREAMING_SNAKE_CASE										=   image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

    return resized_image





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   F.to_tensor(A__       )
    SCREAMING_SNAKE_CASE										=   F.normalize(A__			,  mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,  std=[0.2_2_9, 0.2_2_4, 0.2_2_5]       )
    return image





@torch.no_grad()
def 	__a     (      A__			:  Union[str, Any]			,  A__			:  int			,  A__			:  str       ):
    logger.info("Converting model..."       )

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

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

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

    SCREAMING_SNAKE_CASE										=   model(A__       )

    if "detection" in checkpoint_url:
        SCREAMING_SNAKE_CASE										=   (1, 15, 3)
        SCREAMING_SNAKE_CASE										=   torch.tensor(
            [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]       )
        SCREAMING_SNAKE_CASE										=   torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]       )

    else:
        SCREAMING_SNAKE_CASE										=   (1, 125, 7)
        SCREAMING_SNAKE_CASE										=   torch.tensor(
            [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]       )
        SCREAMING_SNAKE_CASE										=   torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]       )

    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..."       )
        SCREAMING_SNAKE_CASE										=   (
            "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__":
    __A			:					Optional[int]						=			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.'
    )
    __A			:					Optional[Any]						=			parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))