



'''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()
__a          =		logging.get_logger(__name__)





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

      return new_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Tuple:
      # for each of the encoder blocks:
      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__  :    Tuple    						=      state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"		)
                  snake_case__  :    List[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
                  snake_case__  :    str    						=      kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  snake_case__  :    List[str]    						=      kv_bias[: config.hidden_sizes[i]]
                  snake_case__  :    Union[str, Any]    						=      kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  snake_case__  :    List[str]    						=      kv_bias[config.hidden_sizes[i] :]





def 						__snake_case(				)		-> Optional[Any]:
      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)

      return image





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False     ,   _lowerCAmelCase=None		)		-> Optional[int]:
      snake_case__  :    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__  :    Optional[Any]    						=      GLPNImageProcessor()

      # prepare image
      snake_case__  :    Optional[int]    						=      prepare_img()
      snake_case__  :    Optional[Any]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		).pixel_values

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

      # load original state dict
      snake_case__  :    List[Any]    						=      torch.load(_lowerCAmelCase     ,   map_location=torch.device("""cpu"""		)		)

      # rename keys
      snake_case__  :    str    						=      rename_keys(_lowerCAmelCase		)

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

      # create HuggingFace model and load state dict
      snake_case__  :    int    						=      GLPNForDepthEstimation(_lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)
      model.eval()

      # forward pass
      snake_case__  :    int    						=      model(_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  snake_case__  :    Dict    						=      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__  :    Optional[int]    						=      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__  :    List[str]    						=      torch.Size([1, 480, 640]		)

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


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







def 			A__							(    SCREAMING_SNAKE_CASE__ = 200)   ->    int:
  __snake_case:						Optional[int]   =						[1, 2, 5, 10, 20, 50, 100, 200]
  __snake_case:						List[Any]   =						[0] * (pence + 1)
  __snake_case:						int   =						1  # base case: 1 way to make 0 pence

  for coin in coins:
    for i in range(SCREAMING_SNAKE_CASE__					,					pence + 1					,					1):
      number_of_ways[i] += number_of_ways[i - coin]
  return number_of_ways[pence]


if __name__ == "__main__":
      assert solution(200) == 73_682






