


"""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()
__snake_case   			=    logging.get_logger(__name__)







def   __lowerCAmelCase						(						lowercase  :  Any )	->						List[str]:


    """simple docstring"""





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

    return new_state_dict







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  str )	->						Union[str, Any]:


    """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)
            snake_case    :      str      					=    state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
            snake_case    :      Dict      					=    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
            snake_case    :      Optional[int]      					=    kv_weight[
                : config.hidden_sizes[i], :
            ]
            snake_case    :      Dict      					=    kv_bias[: config.hidden_sizes[i]]
            snake_case    :      str      					=    kv_weight[
                config.hidden_sizes[i] :, :
            ]
            snake_case    :      List[str]      					=    kv_bias[config.hidden_sizes[i] :]







def   __lowerCAmelCase						(						)	->						int:


    """simple docstring"""





    snake_case    :      List[str]      					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case    :      Dict      					=    Image.open(requests.get(lowercase						,							stream=lowercase ).raw )

    return image







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[str]=False						,							lowercase  :  Union[str, Any]=None )	->						str:


    """simple docstring"""





    snake_case    :      List[str]      					=    GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,							decoder_hidden_size=64						,							depths=[3, 8, 27, 3] )

    # load image processor (only resize + rescale)
    snake_case    :      List[Any]      					=    GLPNImageProcessor()

    # prepare image
    snake_case    :      Dict      					=    prepare_img()
    snake_case    :      int      					=    image_processor(images=lowercase						,							return_tensors="pt" ).pixel_values

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

    # load original state dict
    snake_case    :      Dict      					=    torch.load(lowercase						,							map_location=torch.device("cpu" ) )

    # rename keys
    snake_case    :      Union[str, Any]      					=    rename_keys(lowercase )

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

    # create HuggingFace model and load state dict
    snake_case    :      Dict      					=    GLPNForDepthEstimation(lowercase )
    model.load_state_dict(lowercase )
    model.eval()

    # forward pass
    snake_case    :      int      					=    model(lowercase )
    snake_case    :      Dict      					=    outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            snake_case    :      str      					=    torch.tensor(
                [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
        elif "kitti" in model_name:
            snake_case    :      Dict      					=    torch.tensor(
                [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
        else:
            raise ValueError(F'Unknown model name: {model_name}' )

        snake_case    :      Dict      					=    torch.Size([1, 480, 640] )

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]						,							lowercase						,							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(lowercase						,							lowercase )						,							organization="nielsr"						,							commit_message="Add model"						,							use_temp_dir=lowercase						,							)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowercase						,							lowercase )						,							organization="nielsr"						,							commit_message="Add image processor"						,							use_temp_dir=lowercase						,							)


if __name__ == "__main__":
  __snake_case   			=    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.""",
  )
  __snake_case   			=    parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


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





