


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

      return new_state_dict




def   snake_case		(     UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Tuple    )   ->      Tuple:






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




def   snake_case		(     )   ->      List[Any]:
      lowerCamelCase					:					Optional[Any]        =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCamelCase					:					Tuple        =	Image.open(requests.get(_snake_case			,				stream=_snake_case    ).raw    )

      return image




@torch.no_grad()
def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple=False			,				UpperCamelCase__       :						str=None    )   ->      Any:
      lowerCamelCase					:					Dict        =	GLPNConfig(hidden_sizes=[64, 128, 320, 512]			,				decoder_hidden_size=64			,				depths=[3, 8, 27, 3]    )

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

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

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

      # load original state dict
      lowerCamelCase					:					Optional[Any]        =	torch.load(_snake_case			,				map_location=torch.device("""cpu"""    )    )

      # rename keys
      lowerCamelCase					:					List[str]        =	rename_keys(_snake_case    )

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

      # create HuggingFace model and load state dict
      lowerCamelCase					:					Optional[int]        =	GLPNForDepthEstimation(_snake_case    )
      model.load_state_dict(_snake_case    )
      model.eval()

      # forward pass
      lowerCamelCase					:					Tuple        =	model(_snake_case    )
      lowerCamelCase					:					Tuple        =	outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCamelCase					:					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:
                  lowerCamelCase					:					List[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}'    )

            lowerCamelCase					:					List[Any]        =	torch.Size([1, 480, 640]    )

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


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[Any]	    =		parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








"""simple docstring"""


__lowerCamelCase      :List[Any]	    =		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__lowerCamelCase      :Union[str, Any]	    =		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					Tuple        =	True
      lowerCamelCase					:					Any        =	[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      order.append(UpperCamelCase__    )

      return order




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					List[Any]        =	True
      lowerCamelCase					:					int        =	[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      return component






def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]    )   ->      list[list[int]]:
      lowerCamelCase					:					int        =	len(UpperCamelCase__    ) * [False]
      lowerCamelCase					:					dict[int, list[int]]        =	{vert: [] for vert in range(len(UpperCamelCase__    )    )}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCamelCase__    )

      lowerCamelCase					:					int        =	[]
      for i, was_visited in enumerate(UpperCamelCase__    ):
            if not was_visited:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      lowerCamelCase					:					Tuple        =	[]
      lowerCamelCase					:					str        =	len(UpperCamelCase__    ) * [False]

      for i in range(len(UpperCamelCase__    )    ):
            lowerCamelCase					:					Any        =	order[len(UpperCamelCase__    ) - i - 1]
            if not visited[vert]:
                  lowerCamelCase					:					List[str]        =	find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
                  components_list.append(UpperCamelCase__    )

      return components_list




