






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__							=     logging.get_logger(__name__)






def   UpperCamelCase  (       snake_case__     :   Tuple  ):




     '''simple docstring'''






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

     return new_state_dict






def   UpperCamelCase  (       snake_case__     :   Tuple	,snake_case__     :   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			:Dict          =						state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''  )
               __snake_case			:Tuple          =						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			:Any          =						kv_weight[
                   : config.hidden_sizes[i], :
               ]
               __snake_case			:Union[str, Any]          =						kv_bias[: config.hidden_sizes[i]]
               __snake_case			:Tuple          =						kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               __snake_case			:Optional[int]          =						kv_bias[config.hidden_sizes[i] :]






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Any          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __snake_case			:Dict          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,stream=_SCREAMING_SNAKE_CASE  ).raw  )

     return image






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   List[str]	,snake_case__     :   Union[str, Any]	,snake_case__     :   int=False	,snake_case__     :   List[Any]=None  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						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			:int          =						prepare_img()
     __snake_case			:str          =						image_processor(images=_SCREAMING_SNAKE_CASE	,return_tensors="""pt"""  ).pixel_values

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

     # load original state dict
     __snake_case			:int          =						torch.load(_SCREAMING_SNAKE_CASE	,map_location=torch.device("""cpu"""  )  )

     # rename keys
     __snake_case			:List[str]          =						rename_keys(_SCREAMING_SNAKE_CASE  )

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

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

     # forward pass
     __snake_case			:str          =						model(_SCREAMING_SNAKE_CASE  )
     __snake_case			:Tuple          =						outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               __snake_case			:Optional[int]          =						torch.tensor(
                   [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]  )
          elif "kitti" in model_name:
               __snake_case			:Optional[Any]          =						torch.tensor(
                   [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]  )
          else:
               raise ValueError(f'''Unknown model name: {model_name}'''  )

          __snake_case			:Optional[int]          =						torch.Size([1, 480, 640]  )

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


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










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__							=     [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     for i in range(9  ):
          if grid[row][i] == n or grid[i][column] == n:
               return False

     for i in range(3  ):
          for j in range(3  ):
               if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                    return False

     return True






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for i in range(9  ):
          for j in range(9  ):
               if grid[i][j] == 0:
                    return i, j
     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,end=""" """  )
          print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("""\nExample grid:\n""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          lowerCamelCase__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


