


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__				:	Tuple					       =					logging.get_logger(__name__)


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

   return new_state_dict


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]					)     ->    List[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)
         lowerCamelCase_						:   Tuple		= state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
         lowerCamelCase_						:   Optional[int]		= 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_						:   Any		= kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCamelCase_						:   Optional[Any]		= kv_bias[: config.hidden_sizes[i]]
         lowerCamelCase_						:   Optional[Any]		= kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCamelCase_						:   Dict		= kv_bias[config.hidden_sizes[i] :]


def    __lowerCamelCase   (      )     ->    Dict:
   lowerCamelCase_						:   Optional[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   List[str]		= Image.open(requests.get(A__						,						stream=A__					).raw					)

   return image


@torch.no_grad()
def    __lowerCamelCase   (      A__						: Union[str, Any]						,						A__						: Optional[int]						,						A__						: int=False						,						A__						: List[Any]=None					)     ->    str:
   lowerCamelCase_						:   Any		= 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_						:   List[str]		= prepare_img()
   lowerCamelCase_						:   List[Any]		= image_processor(images=A__						,						return_tensors="""pt"""					).pixel_values

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

   # load original state dict
   lowerCamelCase_						:   Optional[int]		= torch.load(A__						,						map_location=torch.device("""cpu"""					)					)

   # rename keys
   lowerCamelCase_						:   List[Any]		= rename_keys(A__					)

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

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

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

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCamelCase_						:   Union[str, Any]		= 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:
         lowerCamelCase_						:   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}'''					)

      lowerCamelCase_						:   Tuple		= torch.Size([1, 480, 640]					)

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


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









snake_case__				:	List[Any]					       =					[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

snake_case__				:	Tuple					       =					[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

snake_case__				:	List[Any]					       =					[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

snake_case__				:	List[str]					       =					[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

snake_case__				:	Any					       =					[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

snake_case__				:	int					       =					[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

snake_case__				:	Any					       =					[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

snake_case__				:	Union[str, Any]					       =					[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]





