






"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase_     :				Optional[Any]         =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")

UpperCAmelCase_     :				Dict         =				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_     :				int         =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							lowerCAmelCase__       :






 '''simple docstring'''

 __UpperCamelCase							=						field(
     default="cifar10"       ,	metadata={"help": "Name of a dataset from the datasets package"}				)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}       ,	)
 __UpperCamelCase							=						field(default=UpperCAmelCase__       ,	metadata={"help": "A folder containing the training data."}				)
 __UpperCamelCase							=						field(default=UpperCAmelCase__       ,	metadata={"help": "A folder containing the validation data."}				)
 __UpperCamelCase							=						field(
     default=0.1_5       ,	metadata={"help": "Percent to split off of train for validation."}				)
 __UpperCamelCase							=						field(default=3_2       ,	metadata={"help": "The size of the square patches to use for masking."}				)
 __UpperCamelCase							=						field(
     default=0.6       ,	metadata={"help": "Percentage of patches to mask."}       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     }       ,	)
 def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




     '''simple docstring'''


     SCREAMING_SNAKE_CASE_   :						Any							=	{}
     if self.train_dir is not None:
         SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.train_dir
     if self.validation_dir is not None:
         SCREAMING_SNAKE_CASE_   :						List[str]							=	self.validation_dir
     SCREAMING_SNAKE_CASE_   :						List[Any]							=	data_files if data_files else None




@dataclass
class 							lowerCAmelCase__       :






 '''simple docstring'''

 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
             "checkpoint identifier on the hub. "
             "Don't set if you want to train a model from scratch."
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase__				)}       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "Override some existing default config settings when a model is trained from scratch. Example: "
             "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}       ,	)
 __UpperCamelCase							=						field(
     default="main"       ,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,	)
 __UpperCamelCase							=						field(default=UpperCAmelCase__       ,	metadata={"help": "Name or path of preprocessor config."}				)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={
         "help": (
             "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
         )
     }       ,	)
 __UpperCamelCase							=						field(
     default=UpperCAmelCase__       ,	metadata={"help": "Stride to use for the encoder."}       ,	)




class 							lowerCAmelCase__       :






 '''simple docstring'''

 def __init__(		self  :						int		,			lowercase_  :						Tuple=192		,			lowercase_  :						Tuple=32		,			lowercase_  :						Optional[Any]=4		,			lowercase_  :						Optional[int]=0.6):




     '''simple docstring'''


     SCREAMING_SNAKE_CASE_   :						Tuple							=	input_size
     SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	mask_patch_size
     SCREAMING_SNAKE_CASE_   :						Dict							=	model_patch_size
     SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	mask_ratio

     if self.input_size % self.mask_patch_size != 0:
         raise ValueError('''Input size must be divisible by mask patch size''')
     if self.mask_patch_size % self.model_patch_size != 0:
         raise ValueError('''Mask patch size must be divisible by model patch size''')

     SCREAMING_SNAKE_CASE_   :						int							=	self.input_size // self.mask_patch_size
     SCREAMING_SNAKE_CASE_   :						str							=	self.mask_patch_size // self.model_patch_size

     SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.rand_size**2
     SCREAMING_SNAKE_CASE_   :						List[Any]							=	int(np.ceil(self.token_count * self.mask_ratio))



 def __call__(		self  :						Optional[Any]):




     '''simple docstring'''


     SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.permutation(self.token_count)[: self.mask_count]
     SCREAMING_SNAKE_CASE_   :						Any							=	np.zeros(self.token_count		,			dtype=lowercase_)
     SCREAMING_SNAKE_CASE_   :						Tuple							=	1

     SCREAMING_SNAKE_CASE_   :						Tuple							=	mask.reshape((self.rand_size, self.rand_size))
     SCREAMING_SNAKE_CASE_   :						List[Any]							=	mask.repeat(self.scale		,			axis=0).repeat(self.scale		,			axis=1)

     return torch.tensor(mask.flatten())



def       _A    (__a							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.stack([example['''pixel_values'''] for example in examples]							)
    SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.stack([example['''mask'''] for example in examples]							)
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
    if len(sys.argv							) == 2 and sys.argv[1].endswith('''.json'''							):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
    else:
        SCREAMING_SNAKE_CASE_   :						Dict							=	parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('''run_mim'''					,       __a					,       __a							)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,       datefmt='''%m/%d/%Y %H:%M:%S'''					,       handlers=[logging.StreamHandler(sys.stdout							)]					,       )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    SCREAMING_SNAKE_CASE_   :						Tuple							=	training_args.get_process_log_level()
    logger.setLevel(__a							)
    transformers.utils.logging.set_verbosity(__a							)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
        + f'distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'							)
    logger.info(f'Training/evaluation parameters {training_args}'							)

    # Detecting last checkpoint.
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	None
    if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	get_last_checkpoint(training_args.output_dir							)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
            raise ValueError(
                f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                '''Use --overwrite_output_dir to overcome.'''							)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''							)

    # Initialize our dataset.
    SCREAMING_SNAKE_CASE_   :						str							=	load_dataset(
        data_args.dataset_name					,       data_args.dataset_config_name					,       data_files=data_args.data_files					,       cache_dir=model_args.cache_dir					,       use_auth_token=True if model_args.use_auth_token else None					,       )

    # If we don't have a validation split, split off a percentage of train as validation.
    SCREAMING_SNAKE_CASE_   :						List[str]							=	None if '''validation''' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split					,       __a							) and data_args.train_val_split > 0.0:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	ds['''train'''].train_test_split(data_args.train_val_split							)
        SCREAMING_SNAKE_CASE_   :						List[str]							=	split['''train''']
        SCREAMING_SNAKE_CASE_   :						Any							=	split['''test''']

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    SCREAMING_SNAKE_CASE_   :						List[str]							=	{
        '''cache_dir''': model_args.cache_dir,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.config_name_or_path:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoConfig.from_pretrained(model_args.config_name_or_path					,       **__a							)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoConfig.from_pretrained(model_args.model_name_or_path					,       **__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	CONFIG_MAPPING[model_args.model_type]()
        logger.warning('''You are instantiating a new config instance from scratch.'''							)
        if model_args.config_overrides is not None:
            logger.info(f'Overriding config: {model_args.config_overrides}'							)
            config.update_from_string(model_args.config_overrides							)
            logger.info(f'New config: {config}'							)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(__a					,       '''decoder_type'''							):
        SCREAMING_SNAKE_CASE_   :						Any							=	'''simmim'''

    # adapt config
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_args.image_size if model_args.image_size is not None else config.image_size
    SCREAMING_SNAKE_CASE_   :						List[str]							=	model_args.patch_size if model_args.patch_size is not None else config.patch_size
    SCREAMING_SNAKE_CASE_   :						str							=	(
        model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
    )

    config.update(
        {
            '''image_size''': model_args.image_size,
            '''patch_size''': model_args.patch_size,
            '''encoder_stride''': model_args.encoder_stride,
        }							)

    # create image processor
    if model_args.image_processor_name:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoImageProcessor.from_pretrained(model_args.image_processor_name					,       **__a							)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						Any							=	AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,       **__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path					,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)					,       config=__a					,       cache_dir=model_args.cache_dir					,       revision=model_args.model_revision					,       use_auth_token=True if model_args.use_auth_token else None					,       )
    else:
        logger.info('''Training new model from scratch'''							)
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	AutoModelForMaskedImageModeling.from_config(__a							)

    if training_args.do_train:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ds['''train'''].column_names
    else:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	ds['''validation'''].column_names

    if data_args.image_column_name is not None:
        SCREAMING_SNAKE_CASE_   :						Dict							=	data_args.image_column_name
    elif "image" in column_names:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''image'''
    elif "img" in column_names:
        SCREAMING_SNAKE_CASE_   :						int							=	'''img'''
    else:
        SCREAMING_SNAKE_CASE_   :						Any							=	column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	Compose(
        [
            Lambda(lambda __a							: img.convert('''RGB'''							) if img.mode != "RGB" else img							),
            RandomResizedCrop(model_args.image_size					,       scale=(0.67, 1.0)					,       ratio=(3.0 / 4.0, 4.0 / 3.0)							),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean					,       std=image_processor.image_std							),
        ]							)

    # create mask generator
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	MaskGenerator(
        input_size=model_args.image_size					,       mask_patch_size=data_args.mask_patch_size					,       model_patch_size=model_args.patch_size					,       mask_ratio=data_args.mask_ratio					,       )

    def preprocess_images(__a							):
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	[transforms(__a							) for image in examples[image_column_name]]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[mask_generator() for i in range(len(examples[image_column_name]							)							)]

        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError('''--do_train requires a train dataset'''							)
        if data_args.max_train_samples is not None:
            SCREAMING_SNAKE_CASE_   :						List[Any]							=	ds['''train'''].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
        # Set the training transforms
        ds["train"].set_transform(__a							)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError('''--do_eval requires a validation dataset'''							)
        if data_args.max_eval_samples is not None:
            SCREAMING_SNAKE_CASE_   :						str							=	(
                ds['''validation'''].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
            )
        # Set the validation transforms
        ds["validation"].set_transform(__a							)

    # Initialize our trainer
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	Trainer(
        model=__a					,       args=__a					,       train_dataset=ds['''train'''] if training_args.do_train else None					,       eval_dataset=ds['''validation'''] if training_args.do_eval else None					,       tokenizer=__a					,       data_collator=__a					,       )

    # Training
    if training_args.do_train:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	None
        if training_args.resume_from_checkpoint is not None:
            SCREAMING_SNAKE_CASE_   :						Dict							=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            SCREAMING_SNAKE_CASE_   :						Optional[int]							=	last_checkpoint
        SCREAMING_SNAKE_CASE_   :						Dict							=	trainer.train(resume_from_checkpoint=__a							)
        trainer.save_model()
        trainer.log_metrics('''train'''					,       train_result.metrics							)
        trainer.save_metrics('''train'''					,       train_result.metrics							)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	trainer.evaluate()
        trainer.log_metrics('''eval'''					,       __a							)
        trainer.save_metrics('''eval'''					,       __a							)

    # Write model card and (optionally) push to hub
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{
        '''finetuned_from''': model_args.model_name_or_path,
        '''tasks''': '''masked-image-modeling''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''masked-image-modeling'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**__a							)
    else:
        trainer.create_model_card(**__a							)


if __name__ == "__main__":
  main()










"""simple docstring"""






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



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTConfig(embedding_type='''hybrid'''							)

    if "large" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						int							=	10_24
        SCREAMING_SNAKE_CASE_   :						Dict							=	40_96
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	24
        SCREAMING_SNAKE_CASE_   :						Any							=	16
        SCREAMING_SNAKE_CASE_   :						int							=	[5, 11, 17, 23]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[2_56, 5_12, 10_24, 10_24]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	7_68
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[2_56, 5_12, 7_68, 7_68]
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	1_50
        SCREAMING_SNAKE_CASE_   :						str							=	16
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)
        SCREAMING_SNAKE_CASE_   :						int							=	False
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''project'''

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						str							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	7_68
        SCREAMING_SNAKE_CASE_   :						Tuple							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	1_50
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	16
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						List[str]							=	json.load(open(cached_download(hf_hub_url(__a					,       __a					,       repo_type='''dataset'''							)							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Any							=	[1, 1_50, 4_80, 4_80]

    return config, expected_shape



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






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



def       _A    (__a							)				-> Optional[int]:
    """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
    ):
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.model'''					,       '''dpt.encoder'''							)
    if "pretrained.model" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.model'''					,       '''dpt.embeddings'''							)
    if "patch_embed" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''patch_embed'''					,       ''''''							)
    if "pos_embed" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pos_embed'''					,       '''position_embeddings'''							)
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''attn.proj'''					,       '''attention.output.dense'''							)
    if "proj" in name and "project" not in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''proj'''					,       '''projection'''							)
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''blocks'''					,       '''layer'''							)
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''mlp.fc1'''					,       '''intermediate.dense'''							)
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''mlp.fc2'''					,       '''output.dense'''							)
    if "norm1" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''norm1'''					,       '''layernorm_before'''							)
    if "norm2" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''norm2'''					,       '''layernorm_after'''							)
    if "scratch.output_conv" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''scratch.output_conv'''					,       '''head'''							)
    if "scratch" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''scratch'''					,       '''neck'''							)
    if "layer1_rn" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''layer1_rn'''					,       '''convs.0'''							)
    if "layer2_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''layer2_rn'''					,       '''convs.1'''							)
    if "layer3_rn" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''layer3_rn'''					,       '''convs.2'''							)
    if "layer4_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''layer4_rn'''					,       '''convs.3'''							)
    if "refinenet" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	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
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace(f'refinenet{layer_idx}'					,       f'fusion_stage.layers.{abs(layer_idx-4							)}'							)
    if "out_conv" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''out_conv'''					,       '''projection'''							)
    if "resConfUnit1" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit1'''					,       '''residual_layer1'''							)
    if "resConfUnit2" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit2'''					,       '''residual_layer2'''							)
    if "conv1" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''conv1'''					,       '''convolution1'''							)
    if "conv2" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''conv2'''					,       '''convolution2'''							)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						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:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.act_postprocess2.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.1.0'''							)
    if "pretrained.act_postprocess3.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						List[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:
        SCREAMING_SNAKE_CASE_   :						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:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''pretrained.act_postprocess1.3'''					,       '''neck.reassemble_stage.layers.0.projection'''							)
    if "pretrained.act_postprocess1.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess1.4'''					,       '''neck.reassemble_stage.layers.0.resize'''							)
    if "pretrained.act_postprocess2.3" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''pretrained.act_postprocess2.3'''					,       '''neck.reassemble_stage.layers.1.projection'''							)
    if "pretrained.act_postprocess2.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess2.4'''					,       '''neck.reassemble_stage.layers.1.resize'''							)
    if "pretrained.act_postprocess3.3" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''pretrained.act_postprocess3.3'''					,       '''neck.reassemble_stage.layers.2.projection'''							)
    if "pretrained.act_postprocess4.3" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''pretrained.act_postprocess4.3'''					,       '''neck.reassemble_stage.layers.3.projection'''							)
    if "pretrained.act_postprocess4.4" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.act_postprocess4.4'''					,       '''neck.reassemble_stage.layers.3.resize'''							)
    if "pretrained" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pretrained'''					,       '''dpt'''							)
    if "bn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''bn'''					,       '''batch_norm'''							)
    if "head" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''head'''					,       '''head.head'''							)
    if "encoder.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''encoder.norm'''					,       '''layernorm'''							)
    if "auxlayer" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''auxlayer'''					,       '''auxiliary_head.head'''							)
    if "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''backbone'''					,       '''backbone.bit.encoder'''							)

    if ".." in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''..'''					,       '''.'''							)

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



def       _A    (__a					,       __a							)				-> Dict:
    """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)
        SCREAMING_SNAKE_CASE_   :						Dict							=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'							)
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_weight[: config.hidden_size, :]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_bias[: config.hidden_size]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_   :						int							=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE_   :						Any							=	in_proj_bias[-config.hidden_size :]



def       _A    ()				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	Image.open(requests.get(__a					,       stream=__a							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






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

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

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_   :						Dict							=	4_80 if '''ade''' in checkpoint_url else 3_84
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	DPTImageProcessor(size=__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_img()
    SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processor(__a					,       return_tensors='''pt'''							)

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

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

        Image.fromarray((prediction / prediction.max()) * 2_55							).show()

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

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


if __name__ == "__main__":
  UpperCAmelCase_     :				List[str]         =				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""",
  )

  UpperCAmelCase_     :				List[Any]         =				parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )


