


"""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		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

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

     return new_state_dict
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str]       ):






     """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)
               _a    	=				state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'       )
               _a    	=				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
               _a    	=				kv_weight[
                   : config.hidden_sizes[i], :
               ]
               _a    	=				kv_bias[: config.hidden_sizes[i]]
               _a    	=				kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               _a    	=				kv_bias[config.hidden_sizes[i] :]
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )

     return image
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					List[Any]=False,     _lowerCAmelCase     :					Optional[Any]=None       ):






     """simple docstring"""

     _a    	=				GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12],     decoder_hidden_size=64,     depths=[3, 8, 27, 3]       )

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

     # prepare image
     _a    	=				prepare_img()
     _a    	=				image_processor(images=_lowerCAmelCase,     return_tensors='''pt'''       ).pixel_values

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

     # load original state dict
     _a    	=				torch.load(_lowerCAmelCase,     map_location=torch.device('''cpu'''       )       )

     # rename keys
     _a    	=				rename_keys(_lowerCAmelCase       )

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

     # create HuggingFace model and load state dict
     _a    	=				GLPNForDepthEstimation(_lowerCAmelCase       )
     model.load_state_dict(_lowerCAmelCase       )
     model.eval()

     # forward pass
     _a    	=				model(_lowerCAmelCase       )
     _a    	=				outputs.predicted_depth

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

          _a    	=				torch.Size([1, 4_80, 6_40]       )

          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__":
   __snake_case		=       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		=       parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				BertForPreTraining(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     torch.save(model.state_dict(),     _lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)