



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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:
   snake_case				:    int           =	OrderedDict()
   for key, value in state_dict.items():
      if key.startswith("""module.encoder"""					):
         snake_case				:    Union[str, Any]           =	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				:    Tuple           =	key[key.find("""patch_embed"""					) + len("""patch_embed"""					)]
         snake_case				:    Dict           =	key.replace(f"""patch_embed{idx}"""						,f"""patch_embeddings.{int(lowerCamelCase_					)-1}"""					)
      if "norm" in key:
         snake_case				:    Tuple           =	key.replace("""norm"""						,"""layer_norm"""					)
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         snake_case				:    str           =	key[key.find("""glpn.encoder.layer_norm"""					) + len("""glpn.encoder.layer_norm"""					)]
         snake_case				:    Union[str, Any]           =	key.replace(f"""layer_norm{idx}"""						,f"""layer_norm.{int(lowerCamelCase_					)-1}"""					)
      if "layer_norm1" in key:
         snake_case				:    int           =	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				:    Optional[Any]           =	key[key.find("""block"""					) + len("""block"""					)]
         snake_case				:    Optional[int]           =	key.replace(f"""block{idx}"""						,f"""block.{int(lowerCamelCase_					)-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				:    str           =	key.replace("""attn.proj"""						,"""attention.output.dense"""					)
      if "attn" in key:
         snake_case				:    int           =	key.replace("""attn"""						,"""attention.self"""					)
      if "fc1" in key:
         snake_case				:    Any           =	key.replace("""fc1"""						,"""dense1"""					)
      if "fc2" in key:
         snake_case				:    str           =	key.replace("""fc2"""						,"""dense2"""					)
      if "linear_pred" in key:
         snake_case				:    Optional[Any]           =	key.replace("""linear_pred"""						,"""classifier"""					)
      if "linear_fuse" in key:
         snake_case				:    List[str]           =	key.replace("""linear_fuse.conv"""						,"""linear_fuse"""					)
         snake_case				:    str           =	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				:    List[Any]           =	key.replace(f"""linear_c{idx}"""						,f"""linear_c.{int(lowerCamelCase_					)-1}"""					)
      if "bot_conv" in key:
         snake_case				:    Optional[int]           =	key.replace("""bot_conv"""						,"""0.convolution"""					)
      if "skip_conv1" in key:
         snake_case				:    str           =	key.replace("""skip_conv1"""						,"""1.convolution"""					)
      if "skip_conv2" in key:
         snake_case				:    List[str]           =	key.replace("""skip_conv2"""						,"""2.convolution"""					)
      if "fusion1" in key:
         snake_case				:    List[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				:    Any           =	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				:    Dict           =	value

   return new_state_dict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   # 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				:    List[str]           =	state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""					)
         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
         snake_case				:    List[str]           =	kv_weight[
             : config.hidden_sizes[i], :
         ]
         snake_case				:    List[str]           =	kv_bias[: config.hidden_sizes[i]]
         snake_case				:    Any           =	kv_weight[
             config.hidden_sizes[i] :, :
         ]
         snake_case				:    Optional[Any]           =	kv_bias[config.hidden_sizes[i] :]




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Union[str, 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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=False						,lowercase=None					)  ->       Dict:
   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				:    str           =	GLPNImageProcessor()

   # prepare image
   snake_case				:    List[Any]           =	prepare_img()
   snake_case				:    List[str]           =	image_processor(images=lowerCamelCase_						,return_tensors="""pt"""					).pixel_values

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

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

   # rename keys
   snake_case				:    List[Any]           =	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				:    Union[str, Any]           =	GLPNForDepthEstimation(lowerCamelCase_					)
   model.load_state_dict(lowerCamelCase_					)
   model.eval()

   # forward pass
   snake_case				:    List[Any]           =	model(lowerCamelCase_					)
   snake_case				:    int           =	outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         snake_case				:    Optional[int]           =	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				:    str           =	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				:    Optional[Any]           =	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__":
       lowerCamelCase	:	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.',
       )
       lowerCamelCase	:	List[str]				=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











from math import pow




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase						,lowercase						,)  ->       tuple[int, int]:
   if current_sum == needed_sum:
      # If the sum of the powers is equal to needed_sum, then we have a solution.
      solutions_count += 1
      return current_sum, solutions_count

   snake_case				:    Union[str, Any]           =	int(pow(lowercase						,lowercase					)					)
   if current_sum + i_to_n <= needed_sum:
      # If the sum of the powers is less than needed_sum, then continue adding powers.
      current_sum += i_to_n
      snake_case     , snake_case				:    List[Any]           =	backtrack(
          lowercase						,lowercase						,current_number + 1						,lowercase						,lowercase					)
      current_sum -= i_to_n
   if i_to_n < needed_sum:
      # If the power of i is less than needed_sum, then try with the next power.
      snake_case     , snake_case				:    str           =	backtrack(
          lowercase						,lowercase						,current_number + 1						,lowercase						,lowercase					)
   return current_sum, solutions_count




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
      raise ValueError(
          """Invalid input\n"""
          """needed_sum must be between 1 and 1000, power between 2 and 10."""					)

   return backtrack(lowercase						,lowercase						,1						,0						,0					)[1]  # Return the solutions_count


if __name__ == "__main__":
       import doctest

       doctest.testmod()





