




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



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

      return new_state_dict



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Dict:
      # 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)
                  lowerCAmelCase__      :			List[Any]   =	state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
                  lowerCAmelCase__      :			int   =	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
                  lowerCAmelCase__      :			int   =	kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase__      :			Optional[Any]   =	kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase__      :			Union[str, Any]   =	kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase__      :			str   =	kv_bias[config.hidden_sizes[i] :]



def 	lowercase_				(			)						->    List[str]:
      lowerCAmelCase__      :			str   =	"http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCAmelCase__      :			List[str]   =	Image.open(requests.get(_UpperCAmelCase    ,		stream=_UpperCAmelCase				).raw				)

      return image



@torch.no_grad()
def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=False    ,		__UpperCAmelCase=None				)						->    Tuple:
      lowerCAmelCase__      :			Tuple   =	GLPNConfig(hidden_sizes=[64, 128, 320, 512]    ,		decoder_hidden_size=64    ,		depths=[3, 8, 27, 3]				)

      # load image processor (only resize + rescale)
      lowerCAmelCase__      :			Optional[int]   =	GLPNImageProcessor()

      # prepare image
      lowerCAmelCase__      :			Any   =	prepare_img()
      lowerCAmelCase__      :			Optional[int]   =	image_processor(images=_UpperCAmelCase    ,		return_tensors="""pt"""				).pixel_values

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

      # load original state dict
      lowerCAmelCase__      :			str   =	torch.load(_UpperCAmelCase    ,		map_location=torch.device("""cpu"""				)				)

      # rename keys
      lowerCAmelCase__      :			str   =	rename_keys(_UpperCAmelCase				)

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

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

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

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase__      :			Optional[int]   =	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:
                  lowerCAmelCase__      :			Union[str, Any]   =	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}"""				)

            lowerCAmelCase__      :			Any   =	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__":
 _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)











"""simple docstring"""


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A													=      logging.get_logger(__name__)

_A													=      {"""vocab_file""": """vocab.json"""}

_A													=      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_A													=      {"""mgp-str""": 2_7}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Optional[int]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Union[str, Any]="[GO]"   ,     UpperCamelCase       :		Any="[GO]"   ,     UpperCamelCase       :		Tuple="[s]"   ,     UpperCamelCase       :		List[Any]="[GO]"   ,     **UpperCamelCase       :		Dict   )			->						Union[str, Any]:

         """simple docstring"""
         super().__init__(
             unk_token=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     **UpperCamelCase   ,     )

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			Optional[Any]   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{v: k for k, v in self.vocab.items()}






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         return len(self.vocab   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Any:

         """simple docstring"""
         return dict(self.vocab   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	[]
         for s in text:
               char_tokens.extend(UpperCamelCase   )
         return char_tokens






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		str   )			->						Tuple:

         """simple docstring"""
         return self.vocab.get(UpperCamelCase   ,     self.vocab.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		int   )			->						Any:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase   )   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.vocab   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         return (vocab_file,)





