

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_			=  logging.get_logger(__name__)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Tuple				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       SwinConfig(
        embed_dim=192   ,      depths=(2, 2, 18, 2)   ,      num_heads=(6, 12, 24, 48)   ,      window_size=12   ,      out_features=['stage2', 'stage3', 'stage4']   ,      )

    A__   				=       DetaConfig(
        backbone_config=SCREAMING_SNAKE_CASE__   ,      num_queries=900   ,      encoder_ffn_dim=2048   ,      decoder_ffn_dim=2048   ,      num_feature_levels=5   ,      assign_first_stage=SCREAMING_SNAKE_CASE__   ,      with_box_refine=SCREAMING_SNAKE_CASE__   ,      two_stage=SCREAMING_SNAKE_CASE__   ,      )

    # set labels
    A__   				=       'huggingface/label-files'
    if "o365" in model_name:
        A__   				=       366
        A__   				=       'object365-id2label.json'
    else:
        A__   				=       91
        A__   				=       'coco-detection-id2label.json'

    A__   				=       num_labels
    A__   				=       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      repo_type='dataset'				)				)   ,      'r'				)				)
    A__   				=       {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
    A__   				=       idalabel
    A__   				=       {v: k for k, v in idalabel.items()}

    return config
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				int:





    '''simple docstring'''






    A__   				=       []

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')				)
    rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')				)
    rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')				)
    rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')				)
    # stages
    for i in range(len(config.backbone_config.depths				)				):
        for j in range(config.backbone_config.depths[i]				):
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')				)

        if i < 3:
            rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')				)
            rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')				)

    rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')				)
    rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')				)
    rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')				)
    rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')				)
    rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')				)
    rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')				)

    # transformer encoder
    for i in range(config.encoder_layers				):
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')				)

    # transformer decoder
    for i in range(config.decoder_layers				):
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')				)

    # fmt: on

    return rename_keys
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Dict:





    '''simple docstring'''






    A__   				=       dct.pop(SCREAMING_SNAKE_CASE__				)
    A__   				=       val
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
    for i in range(len(backbone_config.depths				)				):
        A__   				=       num_features[i]
        for j in range(backbone_config.depths[i]				):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            A__   				=       state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'				)
            A__   				=       state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'				)
            # next, add query, keys and values (in that order) to the state dict
            A__   				=       in_proj_weight[:dim, :]
            A__   				=       in_proj_bias[: dim]
            A__   				=       in_proj_weight[
                dim : dim * 2, :
            ]
            A__   				=       in_proj_bias[
                dim : dim * 2
            ]
            A__   				=       in_proj_weight[
                -dim :, :
            ]
            A__   				=       in_proj_bias[-dim :]
            # fmt: on
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Any				)							->				Dict:





    '''simple docstring'''






    A__   				=       config.d_model
    for i in range(config.decoder_layers				):
        # read in weights + bias of input projection layer of self-attention
        A__   				=       state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'				)
        A__   				=       state_dict.pop(f'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[:hidden_size, :]
        A__   				=       in_proj_bias[:hidden_size]
        A__   				=       in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        A__   				=       in_proj_bias[hidden_size : hidden_size * 2]
        A__   				=       in_proj_weight[-hidden_size:, :]
        A__   				=       in_proj_bias[-hidden_size:]
def 						_snake_case(   )							->				Dict:





    '''simple docstring'''






    A__   				=       'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__   				=       Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,      stream=SCREAMING_SNAKE_CASE__				).raw				)

    return im
@torch.no_grad()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				List[str]:





    '''simple docstring'''






    A__   				=       get_deta_config(SCREAMING_SNAKE_CASE__				)

    # load original state dict
    if model_name == "deta-swin-large":
        A__   				=       hf_hub_download(repo_id='nielsr/deta-checkpoints'   ,      filename='adet_swin_ft.pth'				)
    elif model_name == "deta-swin-large-o365":
        A__   				=       hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'   ,      filename='deta_swin_pt_o365.pth'				)
    else:
        raise ValueError(f'Model name {model_name} not supported'				)

    A__   				=       torch.load(SCREAMING_SNAKE_CASE__   ,      map_location='cpu'				)['model']

    # original state dict
    for name, param in state_dict.items():
        print(SCREAMING_SNAKE_CASE__   ,      param.shape				)

    # rename keys
    A__   				=       create_rename_keys(SCREAMING_SNAKE_CASE__				)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__   ,      config.backbone_config				)
    read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            A__   				=       state_dict.pop(SCREAMING_SNAKE_CASE__				)
            A__   				=       val
        if "input_proj" in key:
            A__   				=       state_dict.pop(SCREAMING_SNAKE_CASE__				)
            A__   				=       val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            A__   				=       state_dict.pop(SCREAMING_SNAKE_CASE__				)
            A__   				=       val

    # finally, create HuggingFace model and load state dict
    A__   				=       DetaForObjectDetection(SCREAMING_SNAKE_CASE__				)
    model.load_state_dict(SCREAMING_SNAKE_CASE__				)
    model.eval()

    A__   				=       'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(SCREAMING_SNAKE_CASE__				)

    # load image processor
    A__   				=       DetaImageProcessor(format='coco_detection'				)

    # verify our conversion on image
    A__   				=       prepare_img()
    A__   				=       processor(images=SCREAMING_SNAKE_CASE__   ,      return_tensors='pt'				)
    A__   				=       encoding['pixel_values']
    A__   				=       model(pixel_values.to(SCREAMING_SNAKE_CASE__				)				)

    # verify logits
    print('Logits:'   ,      outputs.logits[0, :3, :3]				)
    print('Boxes:'   ,      outputs.pred_boxes[0, :3, :3]				)
    if model_name == "deta-swin-large":
        A__   				=       torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]				)
        A__   				=       torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]				)
    elif model_name == "deta-swin-large-o365":
        A__   				=       torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]				)
        A__   				=       torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]				)
    assert torch.allclose(outputs.logits[0, :3, :3]   ,      expected_logits.to(SCREAMING_SNAKE_CASE__				)   ,      atol=1E-4				)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,      expected_boxes.to(SCREAMING_SNAKE_CASE__				)   ,      atol=1E-4				)
    print('Everything ok!'				)

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'				)
        Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
        model.save_pretrained(SCREAMING_SNAKE_CASE__				)
        processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

    # Push to hub
    if push_to_hub:
        print('Pushing model and processor to hub...'				)
        model.push_to_hub(f'jozhang97/{model_name}'				)
        processor.push_to_hub(f'jozhang97/{model_name}'				)


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         type=str,
         default="deta-swin-large",
         choices=["deta-swin-large", "deta-swin-large-o365"],
         help="Name of the model 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."
     )
     lowercase_			=  parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

