





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		["""image_processor""", """tokenizer"""]
 UpperCAmelCase__													=		"""CLIPImageProcessor"""
 UpperCAmelCase__													=		("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
 def __init__(      self  :      str			,     UpperCAmelCase  :      Optional[int]=None			,     UpperCAmelCase  :      List[str]=None			,     **UpperCAmelCase  :      Any	)			->	List[str]:
     lowerCamelCase__    :      Optional[Any]					 =		None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'			,     UpperCAmelCase			,     )
         lowerCamelCase__    :      str					 =		kwargs.pop('feature_extractor'	)

     lowerCamelCase__    :      Any					 =		image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'	)
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'	)

     super().__init__(UpperCAmelCase			,     UpperCAmelCase	)
 def __call__(      self  :      List[str]			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      List[Any]=None			,     **UpperCAmelCase  :      int	)			->	Optional[int]:

     if text is None and images is None:
         raise ValueError('You have to specify either text or images. Both cannot be none.'	)

     if text is not None:
         lowerCamelCase__    :      List[str]					 =		self.tokenizer(UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)

     if images is not None:
         lowerCamelCase__    :      List[str]					 =		self.image_processor(UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)

     if text is not None and images is not None:
         lowerCamelCase__    :      Optional[Any]					 =		image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase	)			,     tensor_type=UpperCAmelCase	)
 def      A_	(      self  :      int			,     *UpperCAmelCase  :      Union[str, Any]			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     return self.tokenizer.batch_decode(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Tuple	)			->	Optional[int]:
     return self.tokenizer.decode(*UpperCAmelCase			,     **UpperCAmelCase	)







 @property
 def      A_	(      self  :      Any	)			->	List[Any]:
     lowerCamelCase__    :      Optional[int]					 =		self.tokenizer.model_input_names
     lowerCamelCase__    :      Tuple					 =		self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)












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

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     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)
            lowerCamelCase__    :      Any					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      Optional[Any]					 =		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__    :      Optional[int]					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      Optional[int]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Any					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      Dict					 =		kv_bias[config.hidden_sizes[i] :]


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      List[str]					 =		'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 )			->     Optional[int]:
    lowerCamelCase__    :      str					 =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,	decoder_hidden_size=64						,	depths=[3, 8, 27, 3] )

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

    # prepare image
    lowerCamelCase__    :      str					 =		prepare_img()
    lowerCamelCase__    :      Tuple					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

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

    # load original state dict
    lowerCamelCase__    :      Any					 =		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__    :      Dict					 =		GLPNForDepthEstimation(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # forward pass
    lowerCamelCase__    :      List[str]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__    :      List[Any]					 =		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__    :      List[str]					 =		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__    :      Tuple					 =		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				:       int	   =				parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





