






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case												=  logging.get_logger(__name__)


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]       ):
    UpperCamelCase       :Any								=   DPTConfig(embedding_type='''hybrid'''       )

    if "large" in checkpoint_url:
        UpperCamelCase       :Any								=   1024
        UpperCamelCase       :Tuple								=   4096
        UpperCamelCase       :List[Any]								=   24
        UpperCamelCase       :int								=   16
        UpperCamelCase       :Tuple								=   [5, 11, 17, 23]
        UpperCamelCase       :Optional[Any]								=   [256, 512, 1024, 1024]
        UpperCamelCase       :str								=   (1, 384, 384)

    if "nyu" or "midas" in checkpoint_url:
        UpperCamelCase       :Union[str, Any]								=   768
        UpperCamelCase       :int								=   [1, 1, 1, 0.5]
        UpperCamelCase       :List[str]								=   [256, 512, 768, 768]
        UpperCamelCase       :int								=   150
        UpperCamelCase       :Any								=   16
        UpperCamelCase       :Any								=   (1, 384, 384)
        UpperCamelCase       :List[str]								=   False
        UpperCamelCase       :Union[str, Any]								=   '''project'''

    if "ade" in checkpoint_url:
        UpperCamelCase       :Any								=   True
        UpperCamelCase       :Any								=   768
        UpperCamelCase       :Optional[int]								=   [1, 1, 1, 0.5]
        UpperCamelCase       :Union[str, Any]								=   150
        UpperCamelCase       :Any								=   16
        UpperCamelCase       :Any								=   '''huggingface/label-files'''
        UpperCamelCase       :Any								=   '''ade20k-id2label.json'''
        UpperCamelCase       :str								=   json.load(open(cached_download(hf_hub_url(__UpperCamelCase     ,  __UpperCamelCase     ,  repo_type='''dataset'''       )       )     ,  '''r'''       )       )
        UpperCamelCase       :Optional[Any]								=   {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
        UpperCamelCase       :Optional[Any]								=   idalabel
        UpperCamelCase       :Optional[int]								=   {v: k for k, v in idalabel.items()}
        UpperCamelCase       :List[Any]								=   [1, 150, 480, 480]

    return config, expected_shape


def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
    UpperCamelCase       :int								=   ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(__UpperCamelCase     ,  __UpperCamelCase       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	int       ):
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        UpperCamelCase       :Dict								=   name.replace('''pretrained.model'''     ,  '''dpt.encoder'''       )
    if "pretrained.model" in name:
        UpperCamelCase       :List[Any]								=   name.replace('''pretrained.model'''     ,  '''dpt.embeddings'''       )
    if "patch_embed" in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''patch_embed'''     ,  ''''''       )
    if "pos_embed" in name:
        UpperCamelCase       :Dict								=   name.replace('''pos_embed'''     ,  '''position_embeddings'''       )
    if "attn.proj" in name:
        UpperCamelCase       :Any								=   name.replace('''attn.proj'''     ,  '''attention.output.dense'''       )
    if "proj" in name and "project" not in name:
        UpperCamelCase       :Tuple								=   name.replace('''proj'''     ,  '''projection'''       )
    if "blocks" in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''blocks'''     ,  '''layer'''       )
    if "mlp.fc1" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''mlp.fc1'''     ,  '''intermediate.dense'''       )
    if "mlp.fc2" in name:
        UpperCamelCase       :Union[str, Any]								=   name.replace('''mlp.fc2'''     ,  '''output.dense'''       )
    if "norm1" in name and "backbone" not in name:
        UpperCamelCase       :List[str]								=   name.replace('''norm1'''     ,  '''layernorm_before'''       )
    if "norm2" in name and "backbone" not in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''norm2'''     ,  '''layernorm_after'''       )
    if "scratch.output_conv" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''scratch.output_conv'''     ,  '''head'''       )
    if "scratch" in name:
        UpperCamelCase       :Dict								=   name.replace('''scratch'''     ,  '''neck'''       )
    if "layer1_rn" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''layer1_rn'''     ,  '''convs.0'''       )
    if "layer2_rn" in name:
        UpperCamelCase       :Union[str, Any]								=   name.replace('''layer2_rn'''     ,  '''convs.1'''       )
    if "layer3_rn" in name:
        UpperCamelCase       :List[Any]								=   name.replace('''layer3_rn'''     ,  '''convs.2'''       )
    if "layer4_rn" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''layer4_rn'''     ,  '''convs.3'''       )
    if "refinenet" in name:
        UpperCamelCase       :List[str]								=   int(name[len('''neck.refinenet'''       ) : len('''neck.refinenet'''       ) + 1]       )
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        UpperCamelCase       :Dict								=   name.replace(F'''refinenet{layer_idx}'''     ,  F'''fusion_stage.layers.{abs(layer_idx-4       )}'''       )
    if "out_conv" in name:
        UpperCamelCase       :int								=   name.replace('''out_conv'''     ,  '''projection'''       )
    if "resConfUnit1" in name:
        UpperCamelCase       :Dict								=   name.replace('''resConfUnit1'''     ,  '''residual_layer1'''       )
    if "resConfUnit2" in name:
        UpperCamelCase       :str								=   name.replace('''resConfUnit2'''     ,  '''residual_layer2'''       )
    if "conv1" in name:
        UpperCamelCase       :str								=   name.replace('''conv1'''     ,  '''convolution1'''       )
    if "conv2" in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''conv2'''     ,  '''convolution2'''       )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        UpperCamelCase       :Dict								=   name.replace('''pretrained.act_postprocess1.0.project.0'''     ,  '''neck.reassemble_stage.readout_projects.0.0'''       )
    if "pretrained.act_postprocess2.0.project.0" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''pretrained.act_postprocess2.0.project.0'''     ,  '''neck.reassemble_stage.readout_projects.1.0'''       )
    if "pretrained.act_postprocess3.0.project.0" in name:
        UpperCamelCase       :Tuple								=   name.replace('''pretrained.act_postprocess3.0.project.0'''     ,  '''neck.reassemble_stage.readout_projects.2.0'''       )
    if "pretrained.act_postprocess4.0.project.0" in name:
        UpperCamelCase       :Dict								=   name.replace('''pretrained.act_postprocess4.0.project.0'''     ,  '''neck.reassemble_stage.readout_projects.3.0'''       )

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        UpperCamelCase       :List[Any]								=   name.replace('''pretrained.act_postprocess1.3'''     ,  '''neck.reassemble_stage.layers.0.projection'''       )
    if "pretrained.act_postprocess1.4" in name:
        UpperCamelCase       :Dict								=   name.replace('''pretrained.act_postprocess1.4'''     ,  '''neck.reassemble_stage.layers.0.resize'''       )
    if "pretrained.act_postprocess2.3" in name:
        UpperCamelCase       :List[Any]								=   name.replace('''pretrained.act_postprocess2.3'''     ,  '''neck.reassemble_stage.layers.1.projection'''       )
    if "pretrained.act_postprocess2.4" in name:
        UpperCamelCase       :List[str]								=   name.replace('''pretrained.act_postprocess2.4'''     ,  '''neck.reassemble_stage.layers.1.resize'''       )
    if "pretrained.act_postprocess3.3" in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''pretrained.act_postprocess3.3'''     ,  '''neck.reassemble_stage.layers.2.projection'''       )
    if "pretrained.act_postprocess4.3" in name:
        UpperCamelCase       :List[str]								=   name.replace('''pretrained.act_postprocess4.3'''     ,  '''neck.reassemble_stage.layers.3.projection'''       )
    if "pretrained.act_postprocess4.4" in name:
        UpperCamelCase       :Optional[Any]								=   name.replace('''pretrained.act_postprocess4.4'''     ,  '''neck.reassemble_stage.layers.3.resize'''       )
    if "pretrained" in name:
        UpperCamelCase       :Tuple								=   name.replace('''pretrained'''     ,  '''dpt'''       )
    if "bn" in name:
        UpperCamelCase       :Dict								=   name.replace('''bn'''     ,  '''batch_norm'''       )
    if "head" in name:
        UpperCamelCase       :Any								=   name.replace('''head'''     ,  '''head.head'''       )
    if "encoder.norm" in name:
        UpperCamelCase       :Tuple								=   name.replace('''encoder.norm'''     ,  '''layernorm'''       )
    if "auxlayer" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''auxlayer'''     ,  '''auxiliary_head.head'''       )
    if "backbone" in name:
        UpperCamelCase       :List[Any]								=   name.replace('''backbone'''     ,  '''backbone.bit.encoder'''       )

    if ".." in name:
        UpperCamelCase       :List[Any]								=   name.replace('''..'''     ,  '''.'''       )

    if "stem.conv" in name:
        UpperCamelCase       :str								=   name.replace('''stem.conv'''     ,  '''bit.embedder.convolution'''       )
    if "blocks" in name:
        UpperCamelCase       :List[str]								=   name.replace('''blocks'''     ,  '''layers'''       )
    if "convolution" in name and "backbone" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''convolution'''     ,  '''conv'''       )
    if "layer" in name and "backbone" in name:
        UpperCamelCase       :Optional[int]								=   name.replace('''layer'''     ,  '''layers'''       )
    if "backbone.bit.encoder.bit" in name:
        UpperCamelCase       :Union[str, Any]								=   name.replace('''backbone.bit.encoder.bit'''     ,  '''backbone.bit'''       )
    if "embedder.conv" in name:
        UpperCamelCase       :str								=   name.replace('''embedder.conv'''     ,  '''embedder.convolution'''       )
    if "backbone.bit.encoder.stem.norm" in name:
        UpperCamelCase       :Dict								=   name.replace('''backbone.bit.encoder.stem.norm'''     ,  '''backbone.bit.embedder.norm'''       )
    return name


def   _A   (				SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	List[str]       ):
    for i in range(config.num_hidden_layers       ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCamelCase       :Dict								=   state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
        UpperCamelCase       :Tuple								=   state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''       )
        # next, add query, keys and values (in that order) to the state dict
        UpperCamelCase       :str								=   in_proj_weight[: config.hidden_size, :]
        UpperCamelCase       :str								=   in_proj_bias[: config.hidden_size]
        UpperCamelCase       :Optional[Any]								=   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase       :int								=   in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCamelCase       :Any								=   in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase       :Dict								=   in_proj_bias[-config.hidden_size :]


def   _A   (				):
    UpperCamelCase       :str								=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase       :Dict								=   Image.open(requests.get(__UpperCamelCase     ,  stream=__UpperCamelCase       ).raw       )
    return im


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[int]     ,  SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Optional[int]     ,  SCREAMING_SNAKE_CASE__  :	Optional[int]     ,  SCREAMING_SNAKE_CASE__  :	str       ):
    UpperCamelCase       :Any								=   get_dpt_config(__UpperCamelCase       )
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    UpperCamelCase       :List[str]								=   torch.load(__UpperCamelCase     ,  map_location='''cpu'''       )
    # remove certain keys
    remove_ignore_keys_(__UpperCamelCase       )
    # rename keys
    for key in state_dict.copy().keys():
        UpperCamelCase       :Any								=   state_dict.pop(__UpperCamelCase       )
        UpperCamelCase       :Optional[Any]								=   val
    # read in qkv matrices
    read_in_q_k_v(__UpperCamelCase     ,  __UpperCamelCase       )

    # load HuggingFace model
    UpperCamelCase       :List[Any]								=   DPTForSemanticSegmentation(__UpperCamelCase       ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase       )
    model.load_state_dict(__UpperCamelCase       )
    model.eval()

    # Check outputs on an image
    UpperCamelCase       :Tuple								=   480 if '''ade''' in checkpoint_url else 384
    UpperCamelCase       :Optional[int]								=   DPTImageProcessor(size=__UpperCamelCase       )

    UpperCamelCase       :Union[str, Any]								=   prepare_img()
    UpperCamelCase       :str								=   image_processor(__UpperCamelCase     ,  return_tensors='''pt'''       )

    # forward pass
    UpperCamelCase       :Tuple								=   model(**__UpperCamelCase       ).logits if '''ade''' in checkpoint_url else model(**__UpperCamelCase       ).predicted_depth

    if show_prediction:
        UpperCamelCase       :Optional[Any]								=   (
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1       )     ,  size=(image.size[1], image.size[0])     ,  mode='''bicubic'''     ,  align_corners=__UpperCamelCase     ,  )
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 255       ).show()

    if pytorch_dump_folder_path is not None:
        Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
        print(F'''Saving model to {pytorch_dump_folder_path}'''       )
        model.save_pretrained(__UpperCamelCase       )
        print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
        image_processor.save_pretrained(__UpperCamelCase       )

    if push_to_hub:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )


if __name__ == "__main__":
 __snake_case												=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--checkpoint_url""",
     default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
     type=str,
     help="""URL of the original DPT checkpoint you\'d like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=str,
     required=False,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
 )
 parser.add_argument(
     """--model_name""",
     default="""dpt-large""",
     type=str,
     help="""Name of the model, in case you\'re pushing to the hub.""",
 )
 parser.add_argument(
     """--show_prediction""",
     action="""store_true""",
 )

 __snake_case												=  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





