





"""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()
lowerCamelCase_       =		logging.get_logger(__name__)



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

      return new_state_dict



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



def 					__lowerCamelCase       (							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :Tuple		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)

      return image



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		List[Any]			,       a_					:		List[str]=False			,       a_					:		Optional[Any]=None							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  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)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  GLPNImageProcessor()

      # prepare image
      __SCREAMING_SNAKE_CASE       :Dict		    =  prepare_img()
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  image_processor(images=a_			,       return_tensors='''pt'''							).pixel_values

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

      # load original state dict
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.load(a_			,       map_location=torch.device('''cpu'''							)							)

      # rename keys
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  rename_keys(a_							)

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

      # create HuggingFace model and load state dict
      __SCREAMING_SNAKE_CASE       :Dict		    =  GLPNForDepthEstimation(a_							)
      model.load_state_dict(a_							)
      model.eval()

      # forward pass
      __SCREAMING_SNAKE_CASE       :List[str]		    =  model(a_							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  outputs.predicted_depth

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

            __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.Size([1, 4_80, 6_40]							)

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






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Dict		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :Any		    =  image_size
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :Any		    =  tubelet_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)