





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()
_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)


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

    return new_state_dict


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[str]:
    # 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__					:   str      				=	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__					:   Union[str, Any]      				=	kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__					:   int      				=	kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__					:   Optional[int]      				=	kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__					:   Optional[int]      				=	kv_bias[config.hidden_sizes[i] :]


def  SCREAMING_SNAKE_CASE   ( )    ->  Tuple:
    lowerCamelCase__					:   int      				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__					:   Tuple      				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase				).raw				)

    return image


@torch.no_grad()
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=False		,			_UpperCAmelCase=None				)    ->  Any:
    lowerCamelCase__					:   Optional[int]      				=	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__					:   Dict      				=	prepare_img()
    lowerCamelCase__					:   Union[str, Any]      				=	image_processor(images=_UpperCAmelCase		,			return_tensors='pt'				).pixel_values

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

    # load original state dict
    lowerCamelCase__					:   Optional[Any]      				=	torch.load(_UpperCAmelCase		,			map_location=torch.device('cpu'				)				)

    # rename keys
    lowerCamelCase__					:   int      				=	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__					:   Union[str, Any]      				=	model(_UpperCAmelCase				)
    lowerCamelCase__					:   Dict      				=	outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__					:   Optional[int]      				=	torch.tensor(
                [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]				)
        elif "kitti" in model_name:
            lowerCamelCase__					:   Union[str, Any]      				=	torch.tensor(
                [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]				)
        else:
            raise ValueError(F"""Unknown model name: {model_name}"""				)

        lowerCamelCase__					:   Union[str, 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__":
    _UpperCAmelCase       : Tuple			     =       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.""",
    )
    _UpperCAmelCase       : Any			     =       parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)












_UpperCAmelCase       : List[Any]			     =       {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCAmelCase       : Dict			     =       {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  float:
    lowerCamelCase__					:   Dict      				=	from_type.lower().strip('s'				)
    lowerCamelCase__					:   Dict      				=	to_type.lower().strip('s'				)

    lowerCamelCase__					:   Dict      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)
    lowerCamelCase__					:   str      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)

    if from_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   List[Any]      				=	(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    if to_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   Optional[Any]      				=	(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	METRIC_CONVERSION[from_sanitized]
    lowerCamelCase__					:   Optional[int]      				=	METRIC_CONVERSION[to_sanitized]
    lowerCamelCase__					:   List[str]      				=	1

    if from_exponent > to_exponent:
        lowerCamelCase__					:   Dict      				=	from_exponent - to_exponent
    else:
        lowerCamelCase__					:   Dict      				=	-(to_exponent - from_exponent)

    return value * pow(10		,			_UpperCAmelCase				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()





