






"""simple docstring"""






import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_   : Union[str, Any]   			= logging.get_logger(__name__)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('''module.encoder'''							):
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key.replace('''module.encoder'''    ,				'''glpn.encoder'''							)
        if key.startswith('''module.decoder'''							):
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key.replace('''module.decoder'''    ,				'''decoder.stages'''							)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key[key.find('''patch_embed'''							) + len('''patch_embed'''							)]
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		key.replace(f'patch_embed{idx}'    ,				f'patch_embeddings.{int(__a							)-1}'							)
        if "norm" in key:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		key.replace('''norm'''    ,				'''layer_norm'''							)
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key[key.find('''glpn.encoder.layer_norm'''							) + len('''glpn.encoder.layer_norm'''							)]
            SCREAMING_SNAKE_CASE_      :  int    						=		key.replace(f'layer_norm{idx}'    ,				f'layer_norm.{int(__a							)-1}'							)
        if "layer_norm1" in key:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''layer_norm1'''    ,				'''layer_norm_1'''							)
        if "layer_norm2" in key:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''layer_norm2'''    ,				'''layer_norm_2'''							)
        if "block" in key:
            # replace for example block1 by block.0
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		key[key.find('''block'''							) + len('''block'''							)]
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		key.replace(f'block{idx}'    ,				f'block.{int(__a							)-1}'							)
        if "attn.q" in key:
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key.replace('''attn.q'''    ,				'''attention.self.query'''							)
        if "attn.proj" in key:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		key.replace('''attn.proj'''    ,				'''attention.output.dense'''							)
        if "attn" in key:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''attn'''    ,				'''attention.self'''							)
        if "fc1" in key:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''fc1'''    ,				'''dense1'''							)
        if "fc2" in key:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''fc2'''    ,				'''dense2'''							)
        if "linear_pred" in key:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key.replace('''linear_pred'''    ,				'''classifier'''							)
        if "linear_fuse" in key:
            SCREAMING_SNAKE_CASE_      :  str    						=		key.replace('''linear_fuse.conv'''    ,				'''linear_fuse'''							)
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		key.replace('''linear_fuse.bn'''    ,				'''batch_norm'''							)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		key[key.find('''linear_c'''							) + len('''linear_c'''							)]
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key.replace(f'linear_c{idx}'    ,				f'linear_c.{int(__a							)-1}'							)
        if "bot_conv" in key:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		key.replace('''bot_conv'''    ,				'''0.convolution'''							)
        if "skip_conv1" in key:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		key.replace('''skip_conv1'''    ,				'''1.convolution'''							)
        if "skip_conv2" in key:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key.replace('''skip_conv2'''    ,				'''2.convolution'''							)
        if "fusion1" in key:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		key.replace('''fusion1'''    ,				'''1.fusion'''							)
        if "fusion2" in key:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		key.replace('''fusion2'''    ,				'''2.fusion'''							)
        if "fusion3" in key:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		key.replace('''fusion3'''    ,				'''3.fusion'''							)
        if "fusion" in key and "conv" in key:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		key.replace('''conv'''    ,				'''convolutional_layer'''							)
        if key.startswith('''module.last_layer_depth'''							):
            SCREAMING_SNAKE_CASE_      :  str    						=		key.replace('''module.last_layer_depth'''    ,				'''head.head'''							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		value

    return new_state_dict



def 					_A			(__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    for i in range(config.num_encoder_blocks							):
        for j in range(config.depths[i]							):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'							)
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'							)
            # next, add keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_      :  str    						=		kv_weight[
                : config.hidden_sizes[i], :
            ]
            SCREAMING_SNAKE_CASE_      :  str    						=		kv_bias[: config.hidden_sizes[i]]
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		kv_bias[config.hidden_sizes[i] :]



def 					_A			() ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		Image.open(requests.get(__a    ,				stream=__a							).raw							)

    return image



@torch.no_grad()
def 					_A			(__a    ,				__a    ,				__a=False    ,				__a=None							) ->       Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]    ,				decoder_hidden_size=64    ,				depths=[3, 8, 27, 3]							)

    # load image processor (only resize + rescale)
    SCREAMING_SNAKE_CASE_      :  str    						=		GLPNImageProcessor()

    # prepare image
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		prepare_img()
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_processor(images=__a    ,				return_tensors='''pt'''							).pixel_values

    logger.info('''Converting model...'''							)

    # load original state dict
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.load(__a    ,				map_location=torch.device('''cpu'''							)							)

    # rename keys
    SCREAMING_SNAKE_CASE_      :  Dict    						=		rename_keys(__a							)

    # key and value matrices need special treatment
    read_in_k_v(__a    ,				__a							)

    # create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		GLPNForDepthEstimation(__a							)
    model.load_state_dict(__a							)
    model.eval()

    # forward pass
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(__a							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            SCREAMING_SNAKE_CASE_      :  str    						=		torch.tensor(
                [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]							)
        elif "kitti" in model_name:
            SCREAMING_SNAKE_CASE_      :  int    						=		torch.tensor(
                [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]							)
        else:
            raise ValueError(f'Unknown model name: {model_name}'							)

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.Size([1, 4_80, 6_40]							)

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]    ,				__a    ,				atol=1e-4							)
        print('''Looks ok!'''							)

    # finally, push to hub if required
    if push_to_hub:
        logger.info('''Pushing model and image processor to the hub...'''							)
        model.push_to_hub(
            repo_path_or_name=Path(__a    ,				__a							)    ,				organization='''nielsr'''    ,				commit_message='''Add model'''    ,				use_temp_dir=__a    ,				)
        image_processor.push_to_hub(
            repo_path_or_name=Path(__a    ,				__a							)    ,				organization='''nielsr'''    ,				commit_message='''Add image processor'''    ,				use_temp_dir=__a    ,				)


if __name__ == "__main__":
  UpperCAmelCase_   : Any   			= argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""",
      default=None,
      type=str,
      help="""Path to the original PyTorch checkpoint (.pth file).""",
  )
  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 to upload the model to the HuggingFace hub."""
  )
  parser.add_argument(
      """--model_name""",
      default="""glpn-kitti""",
      type=str,
      help="""Name of the model in case you're pushing to the hub.""",
  )
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










"""simple docstring"""






from math import factorial



def 					_A			(__a = 20							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		n // 2

    return int(factorial(__a							) / (factorial(__a							) * factorial(n - k							))							)


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution(20))
  else:
    try:
      UpperCAmelCase_   : List[str]   			= int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number.""")


