
'''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()
snake_case_ :  Any     =					logging.get_logger(__name__)



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

   return new_state_dict



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



def  __snake_case     (    ):
   UpperCamelCase       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCamelCase       =  Image.open(requests.get(_UpperCAmelCase,      stream=_UpperCAmelCase).raw)

   return image



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Any=False,      _UpperCAmelCase					:       str=None):
   UpperCamelCase       =  GLPNConfig(hidden_sizes=[64, 128, 320, 512],      decoder_hidden_size=64,      depths=[3, 8, 27, 3])

   # load image processor (only resize + rescale)
   UpperCamelCase       =  GLPNImageProcessor()

   # prepare image
   UpperCamelCase       =  prepare_img()
   UpperCamelCase       =  image_processor(images=_UpperCAmelCase,      return_tensors='''pt''').pixel_values

   logger.info('''Converting model...''')

   # load original state dict
   UpperCamelCase       =  torch.load(_UpperCAmelCase,      map_location=torch.device('''cpu'''))

   # rename keys
   UpperCamelCase       =  rename_keys(_UpperCAmelCase)

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

   # create HuggingFace model and load state dict
   UpperCamelCase       =  GLPNForDepthEstimation(_UpperCAmelCase)
   model.load_state_dict(_UpperCAmelCase)
   model.eval()

   # forward pass
   UpperCamelCase       =  model(_UpperCAmelCase)
   UpperCamelCase       =  outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         UpperCamelCase       =  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:
         UpperCamelCase       =  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}')

      UpperCamelCase       =  torch.Size([1, 480, 640])

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


if __name__ == "__main__":
   snake_case_ :  Union[str, 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.',
   )
   snake_case_ :  int     =					parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  []
   for data in source_data:
      for i, el in enumerate(_UpperCAmelCase):
         if len(_UpperCAmelCase) < i + 1:
            data_lists.append([])
         data_lists[i].append(float(_UpperCAmelCase))
   return data_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  []
   for dlist, weight in zip(_UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  min(_UpperCAmelCase)
      UpperCamelCase       =  max(_UpperCAmelCase)

      UpperCamelCase       =  []
      # for weight 0 score is 1 - actual score
      if weight == 0:
         for item in dlist:
            try:
               score.append(1 - ((item - mind) / (maxd - mind)))
            except ZeroDivisionError:
               score.append(1)

      elif weight == 1:
         for item in dlist:
            try:
               score.append((item - mind) / (maxd - mind))
            except ZeroDivisionError:
               score.append(0)

        # weight not 0 or 1
      else:
         UpperCamelCase       =  f'Invalid weight of {weight:f} provided'
         raise ValueError(_UpperCAmelCase)

      score_lists.append(_UpperCAmelCase)

   return score_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  [0 for i in range(len(score_lists[0]))]

   for slist in score_lists:
      for j, ele in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  final_scores[j] + ele

   return final_scores



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  get_data(_UpperCAmelCase)
   UpperCamelCase       =  calculate_each_score(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  generate_final_scores(_UpperCAmelCase)

   # append scores to source data
   for i, ele in enumerate(_UpperCAmelCase):
      source_data[i].append(_UpperCAmelCase)

   return source_data





