def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return 1 if input_a == input_a else 0



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert xnor_gate(0						,    0			) == 1
    assert xnor_gate(0						,    1			) == 0
    assert xnor_gate(1						,    0			) == 0
    assert xnor_gate(1						,    1			) == 1


if __name__ == "__main__":
    print(xnor_gate(0, 0))
    print(xnor_gate(0, 1))
    print(xnor_gate(1, 0))
    print(xnor_gate(1, 1))






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()
__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	DPTConfig(embedding_type='''hybrid'''			)

    if "large" in checkpoint_url:
        __UpperCamelCase  :Dict    						=	1_024
        __UpperCamelCase  :Optional[int]    						=	4_096
        __UpperCamelCase  :int    						=	24
        __UpperCamelCase  :List[str]    						=	16
        __UpperCamelCase  :List[str]    						=	[5, 11, 17, 23]
        __UpperCamelCase  :Optional[int]    						=	[256, 512, 1_024, 1_024]
        __UpperCamelCase  :str    						=	(1, 384, 384)

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

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

    return config, expected_shape



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    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  :Optional[int]    						=	name.replace('''pretrained.model'''						,    '''dpt.embeddings'''			)
    if "patch_embed" in name:
        __UpperCamelCase  :Any    						=	name.replace('''patch_embed'''						,    ''''''			)
    if "pos_embed" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pos_embed'''						,    '''position_embeddings'''			)
    if "attn.proj" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''attn.proj'''						,    '''attention.output.dense'''			)
    if "proj" in name and "project" not in name:
        __UpperCamelCase  :Any    						=	name.replace('''proj'''						,    '''projection'''			)
    if "blocks" in name:
        __UpperCamelCase  :List[Any]    						=	name.replace('''blocks'''						,    '''layer'''			)
    if "mlp.fc1" in name:
        __UpperCamelCase  :int    						=	name.replace('''mlp.fc1'''						,    '''intermediate.dense'''			)
    if "mlp.fc2" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''mlp.fc2'''						,    '''output.dense'''			)
    if "norm1" in name and "backbone" not in name:
        __UpperCamelCase  :Dict    						=	name.replace('''norm1'''						,    '''layernorm_before'''			)
    if "norm2" in name and "backbone" not in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''norm2'''						,    '''layernorm_after'''			)
    if "scratch.output_conv" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''scratch.output_conv'''						,    '''head'''			)
    if "scratch" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''scratch'''						,    '''neck'''			)
    if "layer1_rn" in name:
        __UpperCamelCase  :int    						=	name.replace('''layer1_rn'''						,    '''convs.0'''			)
    if "layer2_rn" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer2_rn'''						,    '''convs.1'''			)
    if "layer3_rn" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''layer3_rn'''						,    '''convs.2'''			)
    if "layer4_rn" in name:
        __UpperCamelCase  :int    						=	name.replace('''layer4_rn'''						,    '''convs.3'''			)
    if "refinenet" in name:
        __UpperCamelCase  :Tuple    						=	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  :Union[str, Any]    						=	name.replace('''out_conv'''						,    '''projection'''			)
    if "resConfUnit1" in name:
        __UpperCamelCase  :int    						=	name.replace('''resConfUnit1'''						,    '''residual_layer1'''			)
    if "resConfUnit2" in name:
        __UpperCamelCase  :str    						=	name.replace('''resConfUnit2'''						,    '''residual_layer2'''			)
    if "conv1" in name:
        __UpperCamelCase  :Any    						=	name.replace('''conv1'''						,    '''convolution1'''			)
    if "conv2" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''conv2'''						,    '''convolution2'''			)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        __UpperCamelCase  :Union[str, Any]    						=	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  :Tuple    						=	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  :Any    						=	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  :Tuple    						=	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  :Tuple    						=	name.replace('''pretrained.act_postprocess1.3'''						,    '''neck.reassemble_stage.layers.0.projection'''			)
    if "pretrained.act_postprocess1.4" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess1.4'''						,    '''neck.reassemble_stage.layers.0.resize'''			)
    if "pretrained.act_postprocess2.3" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''pretrained.act_postprocess2.3'''						,    '''neck.reassemble_stage.layers.1.projection'''			)
    if "pretrained.act_postprocess2.4" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess2.4'''						,    '''neck.reassemble_stage.layers.1.resize'''			)
    if "pretrained.act_postprocess3.3" in name:
        __UpperCamelCase  :Dict    						=	name.replace('''pretrained.act_postprocess3.3'''						,    '''neck.reassemble_stage.layers.2.projection'''			)
    if "pretrained.act_postprocess4.3" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained.act_postprocess4.3'''						,    '''neck.reassemble_stage.layers.3.projection'''			)
    if "pretrained.act_postprocess4.4" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''pretrained.act_postprocess4.4'''						,    '''neck.reassemble_stage.layers.3.resize'''			)
    if "pretrained" in name:
        __UpperCamelCase  :Optional[int]    						=	name.replace('''pretrained'''						,    '''dpt'''			)
    if "bn" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''bn'''						,    '''batch_norm'''			)
    if "head" in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''head'''						,    '''head.head'''			)
    if "encoder.norm" in name:
        __UpperCamelCase  :List[str]    						=	name.replace('''encoder.norm'''						,    '''layernorm'''			)
    if "auxlayer" in name:
        __UpperCamelCase  :Optional[Any]    						=	name.replace('''auxlayer'''						,    '''auxiliary_head.head'''			)
    if "backbone" in name:
        __UpperCamelCase  :int    						=	name.replace('''backbone'''						,    '''backbone.bit.encoder'''			)

    if ".." in name:
        __UpperCamelCase  :Tuple    						=	name.replace('''..'''						,    '''.'''			)

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    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  :Any    						=	state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""			)
        __UpperCamelCase  :Dict    						=	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  :Union[str, Any]    						=	in_proj_weight[: config.hidden_size, :]
        __UpperCamelCase  :Dict    						=	in_proj_bias[: config.hidden_size]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __UpperCamelCase  :Dict    						=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __UpperCamelCase  :str    						=	in_proj_weight[
            -config.hidden_size :, :
        ]
        __UpperCamelCase  :Union[str, Any]    						=	in_proj_bias[-config.hidden_size :]



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase  :List[Any]    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			)
    return im



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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

    # load HuggingFace model
    __UpperCamelCase  :Union[str, Any]    						=	DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE			) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE			)
    model.eval()

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

    __UpperCamelCase  :Dict    						=	prepare_img()
    __UpperCamelCase  :Any    						=	image_processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			)

    # forward pass
    __UpperCamelCase  :Optional[Any]    						=	model(**SCREAMING_SNAKE_CASE			).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE			).predicted_depth

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

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

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE			).mkdir(exist_ok=SCREAMING_SNAKE_CASE			)
        print(f"""Saving model to {pytorch_dump_folder_path}"""			)
        model.save_pretrained(SCREAMING_SNAKE_CASE			)
        print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
    __lowercase											=      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''',
    )

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





