
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					:			List[Any]			      =		logging.get_logger(__name__)


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

    return new_state_dict


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    # 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)
            __lowercase		:			Any	    =       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'	)
            __lowercase		:			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
            __lowercase		:			Any	    =       kv_weight[
                : config.hidden_sizes[i], :
            ]
            __lowercase		:			int	    =       kv_bias[: config.hidden_sizes[i]]
            __lowercase		:			Any	    =       kv_weight[
                config.hidden_sizes[i] :, :
            ]
            __lowercase		:			Dict	    =       kv_bias[config.hidden_sizes[i] :]


def     UpperCAmelCase_  (						) ->						Any:
    __lowercase		:			int	    =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase		:			Union[str, Any]	    =       Image.open(requests.get(__lowerCAmelCase	,					stream=__lowerCAmelCase	).raw	)

    return image


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=False	,					__lowerCAmelCase=None	) ->						List[Any]:
    __lowercase		:			int	    =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]	,					decoder_hidden_size=64	,					depths=[3, 8, 27, 3]	)

    # load image processor (only resize + rescale)
    __lowercase		:			List[str]	    =       GLPNImageProcessor()

    # prepare image
    __lowercase		:			List[Any]	    =       prepare_img()
    __lowercase		:			Tuple	    =       image_processor(images=__lowerCAmelCase	,					return_tensors='''pt'''	).pixel_values

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

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

    # rename keys
    __lowercase		:			Dict	    =       rename_keys(__lowerCAmelCase	)

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

    # create HuggingFace model and load state dict
    __lowercase		:			List[Any]	    =       GLPNForDepthEstimation(__lowerCAmelCase	)
    model.load_state_dict(__lowerCAmelCase	)
    model.eval()

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

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

        __lowercase		:			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__":
 __lowerCAmelCase					:			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.",
 )
 __lowerCAmelCase					:			List[Any]			      =		parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__lowerCAmelCase					:			int			      =		{
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__lowerCAmelCase					:			Optional[int]			      =		logging.WARNING


def     UpperCAmelCase_  (						) ->						Optional[Any]:
    __lowercase		:			Any	    =       os.getenv('''DATASETS_VERBOSITY'''	,					__lowerCAmelCase	)
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
                F'has to be one of: { ", ".join(log_levels.keys()	) }'	)
    return _default_log_level


def     UpperCAmelCase_  (						) ->						str:
    return __name__.split('''.'''	)[0]


def     UpperCAmelCase_  (						) ->						logging.Logger:
    return logging.getLogger(_get_library_name()	)


def     UpperCAmelCase_  (						) ->						None:
    # Apply our default configuration to the library root logger.
    __lowercase		:			Union[str, Any]	    =       _get_library_root_logger()
    library_root_logger.setLevel(_get_default_logging_level()	)


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			List[Any]	    =       _get_library_root_logger()
    library_root_logger.setLevel(logging.NOTSET	)


def     UpperCAmelCase_  (						__lowerCAmelCase = None	) ->						logging.Logger:
    if name is None:
        __lowercase		:			Optional[Any]	    =       _get_library_name()
    return logging.getLogger(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						int:
    return _get_library_root_logger().getEffectiveLevel()


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						None:
    _get_library_root_logger().setLevel(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						int:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						List[str]:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						Optional[int]:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						Any:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			Tuple	    =       False


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			Dict	    =       True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       List[Any]				,       *_snake_case		:       List[Any]				,       **_snake_case		:       Dict				):  # pylint: disable=unused-argument
          __lowercase		:			Tuple	    =       args[0] if args else None




      def __iter__(      self		:       Tuple				):
          return iter(self._iterator				)




      def __getattr__(      self		:       Optional[int]				,       _snake_case		:       Union[str, Any]				):

          def empty_fn(*_snake_case		:       Tuple				,       **_snake_case		:       int				):  # pylint: disable=unused-argument
              return

          return empty_fn




      def __enter__(      self		:       Optional[Any]				):
          return self




      def __exit__(      self		:       Optional[Any]				,       _snake_case		:       Any				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          return






__lowerCAmelCase					:			int			      =		True


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __call__(      self		:       List[str]				,       *_snake_case		:       Dict				,       _snake_case		:       str=False				,       **_snake_case		:       List[str]				):
          if _tqdm_active and not disable:
              return tqdm_lib.tqdm(*_snake_case				,       **_snake_case				)
          else:
              return EmptyTqdm(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				,       *_snake_case		:       str				,       **_snake_case		:       List[str]				):
          __lowercase		:			Dict	    =       None
          if _tqdm_active:
              return tqdm_lib.tqdm.set_lock(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          if _tqdm_active:
              return tqdm_lib.tqdm.get_lock()






__lowerCAmelCase					:			Tuple			      =		_tqdm_cls()


def     UpperCAmelCase_  (						) ->						bool:
    global _tqdm_active
    return bool(_tqdm_active	)


def     UpperCAmelCase_  (						) ->						Union[str, Any]:
    global _tqdm_active
    __lowercase		:			Dict	    =       True





def     UpperCAmelCase_  (						) ->						Tuple:
    global _tqdm_active
    __lowercase		:			Optional[int]	    =       False


