



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
A__  :  int								=					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''')

A__  :  str								=					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A__  :  str								=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 				__snake_case						:
							_a			      = field(
							    default='''cifar10'''						,metadata={'''help''': '''Name of a dataset from the datasets package'''}						)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}						,)
							_a			      = field(default=UpperCamelCase_						,metadata={'''help''': '''A folder containing the training data.'''}						)
							_a			      = field(default=UpperCamelCase_						,metadata={'''help''': '''A folder containing the validation data.'''}						)
							_a			      = field(
							    default=0.1_5						,metadata={'''help''': '''Percent to split off of train for validation.'''}						)
							_a			      = field(default=32						,metadata={'''help''': '''The size of the square patches to use for masking.'''}						)
							_a			      = field(
							    default=0.6						,metadata={'''help''': '''Percentage of patches to mask.'''}						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }						,)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     str									= {}
									if self.train_dir is not None:
											lowerCAmelCase_			:     Optional[int]									= self.train_dir
									if self.validation_dir is not None:
											lowerCAmelCase_			:     Optional[Any]									= self.validation_dir
									lowerCAmelCase_			:     int									= data_files if data_files else None





@dataclass
class 				__snake_case						:
							_a			      = 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.'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase_						)}						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
							_a			      = 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'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}						,)
							_a			      = field(
							    default='''main'''						,metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,)
							_a			      = field(default=UpperCamelCase_						,metadata={'''help''': '''Name or path of preprocessor config.'''}						)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={
							        '''help''': (
							            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={
							        '''help''': (
							            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
							        )
							    }						,)
							_a			      = field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Stride to use for the encoder.'''}						,)





class 				__snake_case						:




							def __init__(       self     :       int				,				A_     :       Union[str, Any]=1_9_2				,				A_     :       List[Any]=3_2				,				A_     :       Union[str, Any]=4				,				A_     :       Optional[int]=0.6):
									lowerCAmelCase_			:     Any									= input_size
									lowerCAmelCase_			:     int									= mask_patch_size
									lowerCAmelCase_			:     str									= model_patch_size
									lowerCAmelCase_			:     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''')

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

									lowerCAmelCase_			:     Union[str, Any]									= self.rand_size**2
									lowerCAmelCase_			:     List[Any]									= int(np.ceil(self.token_count * self.mask_ratio))







							def __call__(       self     :       Any):
									lowerCAmelCase_			:     Optional[Any]									= np.random.permutation(self.token_count)[: self.mask_count]
									lowerCAmelCase_			:     Any									= np.zeros(self.token_count				,				dtype=A_)
									lowerCAmelCase_			:     Optional[int]									= 1

									lowerCAmelCase_			:     Union[str, Any]									= mask.reshape((self.rand_size, self.rand_size))
									lowerCAmelCase_			:     Union[str, Any]									= mask.repeat(self.scale				,				axis=0).repeat(self.scale				,				axis=1)

									return torch.tensor(mask.flatten())





def 			UpperCamelCase(  __UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     List[str]									= torch.stack([example['''pixel_values'''] for example in examples] )
		lowerCAmelCase_			:     List[Any]									= torch.stack([example['''mask'''] for example in examples] )
		return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def 			UpperCamelCase(  ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowerCAmelCase_			:     int									= 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.
				lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= 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'''     ,__UpperCamelCase     ,__UpperCamelCase )

		# 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()

		lowerCAmelCase_			:     int									= training_args.get_process_log_level()
		logger.setLevel(__UpperCamelCase )
		transformers.utils.logging.set_verbosity(__UpperCamelCase )
		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.
		lowerCAmelCase_			:     Tuple									= None
		if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
				lowerCAmelCase_			:     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.
		lowerCAmelCase_			:     Dict									= 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.
		lowerCAmelCase_			:     Optional[int]									= None if '''validation''' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split     ,__UpperCamelCase ) and data_args.train_val_split > 0.0:
				lowerCAmelCase_			:     Tuple									= ds['''train'''].train_test_split(data_args.train_val_split )
				lowerCAmelCase_			:     Optional[int]									= split['''train''']
				lowerCAmelCase_			:     Optional[Any]									= split['''test''']

		# Create config
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowerCAmelCase_			:     Optional[Any]									= {
		    '''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:
				lowerCAmelCase_			:     Tuple									= AutoConfig.from_pretrained(model_args.config_name_or_path     ,**__UpperCamelCase )
		elif model_args.model_name_or_path:
				lowerCAmelCase_			:     Optional[Any]									= AutoConfig.from_pretrained(model_args.model_name_or_path     ,**__UpperCamelCase )
		else:
				lowerCAmelCase_			:     int									= 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(__UpperCamelCase     ,'''decoder_type''' ):
				lowerCAmelCase_			:     Union[str, Any]									= '''simmim'''

		# adapt config
		lowerCAmelCase_			:     int									= model_args.image_size if model_args.image_size is not None else config.image_size
		lowerCAmelCase_			:     Tuple									= model_args.patch_size if model_args.patch_size is not None else config.patch_size
		lowerCAmelCase_			:     Any									= (
		    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:
				lowerCAmelCase_			:     List[str]									= AutoImageProcessor.from_pretrained(model_args.image_processor_name     ,**__UpperCamelCase )
		elif model_args.model_name_or_path:
				lowerCAmelCase_			:     Dict									= AutoImageProcessor.from_pretrained(model_args.model_name_or_path     ,**__UpperCamelCase )
		else:
				lowerCAmelCase_			:     Dict									= {
				    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
				}
				lowerCAmelCase_			:     Union[str, Any]									= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

		# create model
		if model_args.model_name_or_path:
				lowerCAmelCase_			:     List[str]									= AutoModelForMaskedImageModeling.from_pretrained(
				    model_args.model_name_or_path     ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path )     ,config=__UpperCamelCase     ,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''' )
				lowerCAmelCase_			:     str									= AutoModelForMaskedImageModeling.from_config(__UpperCamelCase )

		if training_args.do_train:
				lowerCAmelCase_			:     Optional[int]									= ds['''train'''].column_names
		else:
				lowerCAmelCase_			:     Union[str, Any]									= ds['''validation'''].column_names

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

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		lowerCAmelCase_			:     Optional[int]									= Compose(
		    [
		        Lambda(lambda __UpperCamelCase : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
		        RandomResizedCrop(model_args.image_size     ,scale=(0.6_7, 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
		lowerCAmelCase_			:     List[str]									= 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(__UpperCamelCase							:		List[str] ):
				lowerCAmelCase_			:     Optional[int]									= [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
				lowerCAmelCase_			:     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:
						lowerCAmelCase_			:     Tuple									= ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
				# Set the training transforms
				ds["train"].set_transform(__UpperCamelCase )

		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:
						lowerCAmelCase_			:     str									= (
						    ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
						)
				# Set the validation transforms
				ds["validation"].set_transform(__UpperCamelCase )

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

		# Training
		if training_args.do_train:
				lowerCAmelCase_			:     Dict									= None
				if training_args.resume_from_checkpoint is not None:
						lowerCAmelCase_			:     Dict									= training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowerCAmelCase_			:     Tuple									= last_checkpoint
				lowerCAmelCase_			:     str									= trainer.train(resume_from_checkpoint=__UpperCamelCase )
				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:
				lowerCAmelCase_			:     int									= trainer.evaluate()
				trainer.log_metrics('''eval'''     ,__UpperCamelCase )
				trainer.save_metrics('''eval'''     ,__UpperCamelCase )

		# Write model card and (optionally) push to hub
		lowerCAmelCase_			:     List[Any]									= {
		    '''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(**__UpperCamelCase )
		else:
				trainer.create_model_card(**__UpperCamelCase )


if __name__ == "__main__":
						main()










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


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Any									= DPTConfig(embedding_type='''hybrid''' )

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

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

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

		return config, expected_shape


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


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

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

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


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


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


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

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

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

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

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

		if show_prediction:
				lowerCAmelCase_			:     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__":
						A__  :  Optional[int]								=					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''',
						)

						A__  :  Union[str, 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
						)





