





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()
SCREAMING_SNAKE_CASE__       :			int			      =						logging.get_logger(__name__)






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

   return new_state_dict






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   # 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)
         a__	:						str      		=  state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
         a__	:						Dict      		=  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__	:						Union[str, Any]      		=  kv_weight[
             : config.hidden_sizes[i], :
         ]
         a__	:						Union[str, Any]      		=  kv_bias[: config.hidden_sizes[i]]
         a__	:						List[Any]      		=  kv_weight[
             config.hidden_sizes[i] :, :
         ]
         a__	:						List[Any]      		=  kv_bias[config.hidden_sizes[i] :]






def       _A  (      ):
   a__	:						int      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						Union[str, Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






@torch.no_grad()
def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase=False							, lowerCamelCase=None   ):
   a__	:						int      		=  GLPNConfig(hidden_sizes=[64, 128, 320, 512]							, decoder_hidden_size=64							, depths=[3, 8, 27, 3]   )

   # load image processor (only resize + rescale)
   a__	:						Union[str, Any]      		=  GLPNImageProcessor()

   # prepare image
   a__	:						List[str]      		=  prepare_img()
   a__	:						List[Any]      		=  image_processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

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

   # load original state dict
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location=torch.device("cpu"   )   )

   # rename keys
   a__	:						Union[str, Any]      		=  rename_keys(lowerCamelCase   )

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

   # create HuggingFace model and load state dict
   a__	:						List[str]      		=  GLPNForDepthEstimation(lowerCamelCase   )
   model.load_state_dict(lowerCamelCase   )
   model.eval()

   # forward pass
   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						List[Any]      		=  outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         a__	:						Dict      		=  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:
         a__	:						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}"""   )

      a__	:						Union[str, Any]      		=  torch.Size([1, 480, 640]   )

      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__":
  SCREAMING_SNAKE_CASE__       :			int			      =						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.""",
  )
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[str]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						str     		=				PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						List[str]     		=				PRETRAINED_INIT_CONFIGURATION
   _UpperCamelCase :						Optional[int]     		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Optional[Any]     		=				LxmertTokenizer





   def __init__( self					,			snake_case=None					,			snake_case=None					,			snake_case=True					,			snake_case="[UNK]"					,			snake_case="[SEP]"					,			snake_case="[PAD]"					,			snake_case="[CLS]"					,			snake_case="[MASK]"					,			snake_case=True					,			snake_case=None					,			**snake_case					,			)				->  Any:
      """simple docstring"""





      super().__init__(
          snake_case					,			tokenizer_file=snake_case					,			do_lower_case=snake_case					,			unk_token=snake_case					,			sep_token=snake_case					,			pad_token=snake_case					,			cls_token=snake_case					,			mask_token=snake_case					,			tokenize_chinese_chars=snake_case					,			strip_accents=snake_case					,			**snake_case					,			)

      a__	:						Optional[Any]      		=  json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          normalizer_state.get("lowercase"					,			snake_case      ) != do_lower_case
          or normalizer_state.get("strip_accents"					,			snake_case      ) != strip_accents
          or normalizer_state.get("handle_chinese_chars"					,			snake_case      ) != tokenize_chinese_chars
      ):
         a__	:						str      		=  getattr(snake_case					,			normalizer_state.pop("type"      )      )
         a__	:						Tuple      		=  do_lower_case
         a__	:						Union[str, Any]      		=  strip_accents
         a__	:						str      		=  tokenize_chinese_chars
         a__	:						List[str]      		=  normalizer_class(**snake_case      )

      a__	:						str      		=  do_lower_case





   def 	_snake_case ( self					,			snake_case					,			snake_case=None      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      a__	:						Tuple      		=  [self.sep_token_id]
      a__	:						str      		=  [self.cls_token_id]
      if token_ids_a is None:
         return len(cls + token_ids_a + sep      ) * [0]
      return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  self._tokenizer.model.save(snake_case					,			name=snake_case      )
      return tuple(snake_case      )

