


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()
_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __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(lowerCAmelCase__    )-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(lowerCAmelCase__    )-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(lowerCAmelCase__    )-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(lowerCAmelCase__    )-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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : str    ):


     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 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							="""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __UpperCamelCase   							=Image.open(requests.get(lowerCAmelCase__      ,					stream=lowerCAmelCase__    ).raw    )

     return image

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : List[Any]=False      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None    ):
     __UpperCamelCase   							=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)
     __UpperCamelCase   							=GLPNImageProcessor()

     # prepare image
     __UpperCamelCase   							=prepare_img()
     __UpperCamelCase   							=image_processor(images=lowerCAmelCase__      ,					return_tensors='pt'    ).pixel_values

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

     # load original state dict
     __UpperCamelCase   							=torch.load(lowerCAmelCase__      ,					map_location=torch.device('cpu'    )    )

     # rename keys
     __UpperCamelCase   							=rename_keys(lowerCAmelCase__    )

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

     # create HuggingFace model and load state dict
     __UpperCamelCase   							=GLPNForDepthEstimation(lowerCAmelCase__    )
     model.load_state_dict(lowerCAmelCase__    )
     model.eval()

     # forward pass
     __UpperCamelCase   							=model(lowerCAmelCase__    )
     __UpperCamelCase   							=outputs.predicted_depth

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

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









import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_A			=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    UpperCAmelCase__	( nn.Module		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Optional[int]:
         super().__init__()
         __UpperCamelCase   							=torchvision.models.resnetaaa(pretrained=A_   )
         __UpperCamelCase   							=list(model.children()   )[:-2]
         __UpperCamelCase   							=nn.Sequential(*A_   )
         __UpperCamelCase   							=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






    def _a (      self ,       A_   )  ->	int:
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __UpperCamelCase   							=self.pool(self.model(A_   )   )
         __UpperCamelCase   							=torch.flatten(A_ ,       start_dim=2   )
         __UpperCamelCase   							=out.transpose(1 ,       2   ).contiguous()
         return out  # BxNx2048






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=[json.loads(A_   ) for l in open(A_   )]
         __UpperCamelCase   							=os.path.dirname(A_   )
         __UpperCamelCase   							=tokenizer
         __UpperCamelCase   							=labels
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=max_seq_length

         __UpperCamelCase   							=transforms






    def __len__(      self   )  ->	Any:
         return len(self.data   )






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=sentence[0], sentence[1:-1], sentence[-1]
         __UpperCamelCase   							=sentence[: self.max_seq_length]

         __UpperCamelCase   							=torch.zeros(self.n_classes   )
         __UpperCamelCase   							=1

         __UpperCamelCase   							=Image.open(os.path.join(self.data_dir ,       self.data[index]['img']   )   ).convert('RGB'   )
         __UpperCamelCase   							=self.transforms(A_   )

         return {
             "image_start_token": start_token,
             "image_end_token": end_token,
             "sentence": sentence,
             "image": image,
             "label": label,
         }






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=Counter()
         for row in self.data:
              label_freqs.update(row['label']   )
         return label_freqs

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )

     for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    ):
          __UpperCamelCase   							=input_row['sentence']
          __UpperCamelCase   							=1

     __UpperCamelCase   							=torch.stack([row['image'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['label'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['image_start_token'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['image_end_token'] for row in batch]    )

     return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def 						_UpperCAmelCase    (      ):
     return [
         "Crime",
         "Drama",
         "Thriller",
         "Action",
         "Comedy",
         "Romance",
         "Documentary",
         "Short",
         "Mystery",
         "History",
         "Family",
         "Adventure",
         "Fantasy",
         "Sci-Fi",
         "Western",
         "Horror",
         "Sport",
         "War",
         "Music",
         "Musical",
         "Animation",
         "Biography",
         "Film-Noir",
     ]





def 						_UpperCAmelCase    (      ):
     return transforms.Compose(
         [
             transforms.Resize(2_56    ),
             transforms.CenterCrop(2_24    ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46777044, 0.44531429, 0.40661017]      ,					std=[0.12221994, 0.12145835, 0.14380469]      ,					),
         ]    )





