




"""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
__SCREAMING_SNAKE_CASE							:      Union[str, 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')

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





@dataclass
class        __A       :





							'''simple docstring'''

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



							def 						lowerCAmelCase    (			self							: Optional[Any]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						{}
														if self.train_dir is not None:
																					snake_case_          =						self.train_dir
														if self.validation_dir is not None:
																					snake_case_          =						self.validation_dir
														snake_case_          =						data_files if data_files else None





@dataclass
class        __A       :





							'''simple docstring'''

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





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Any						,    UpperCAmelCase_							: Optional[Any]=192						,    UpperCAmelCase_							: List[str]=32						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[str]=0.6    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						input_size
														snake_case_          =						mask_patch_size
														snake_case_          =						model_patch_size
														snake_case_          =						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"""    )

														snake_case_          =						self.input_size // self.mask_patch_size
														snake_case_          =						self.mask_patch_size // self.model_patch_size

														snake_case_          =						self.rand_size**2
														snake_case_          =						int(np.ceil(self.token_count * self.mask_ratio    )    )



							def __call__(			self							: int    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						np.random.permutation(self.token_count    )[: self.mask_count]
														snake_case_          =						np.zeros(self.token_count						,    dtype=__lowerCAmelCase    )
														snake_case_          =						1

														snake_case_          =						mask.reshape((self.rand_size, self.rand_size)    )
														snake_case_          =						mask.repeat(self.scale						,    axis=0    ).repeat(self.scale						,    axis=1    )

														return torch.tensor(mask.flatten()    )







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						torch.stack([example["""pixel_values"""] for example in examples]			)
							snake_case_          =						torch.stack([example["""mask"""] for example in examples]			)
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def   _a			( )      ->			Optional[Any]:
							snake_case_          =						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.
														snake_case_    ,  snake_case_    ,  snake_case_          =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
							else:
														snake_case_    ,  snake_case_    ,  snake_case_          =						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"""			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# 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()

							snake_case_          =						training_args.get_process_log_level()
							logger.setLevel(_SCREAMING_SNAKE_CASE			)
							transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE			)
							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.
							snake_case_          =						None
							if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
														snake_case_          =						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.
							snake_case_          =						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.
							snake_case_          =						None if """validation""" in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split			,	_SCREAMING_SNAKE_CASE			) and data_args.train_val_split > 0.0:
														snake_case_          =						ds["""train"""].train_test_split(data_args.train_val_split			)
														snake_case_          =						split["""train"""]
														snake_case_          =						split["""test"""]

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							snake_case_          =						{
							    """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:
														snake_case_          =						AutoConfig.from_pretrained(model_args.config_name_or_path			,	**_SCREAMING_SNAKE_CASE			)
							elif model_args.model_name_or_path:
														snake_case_          =						AutoConfig.from_pretrained(model_args.model_name_or_path			,	**_SCREAMING_SNAKE_CASE			)
							else:
														snake_case_          =						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(_SCREAMING_SNAKE_CASE			,	"""decoder_type"""			):
														snake_case_          =						"""simmim"""

							# adapt config
							snake_case_          =						model_args.image_size if model_args.image_size is not None else config.image_size
							snake_case_          =						model_args.patch_size if model_args.patch_size is not None else config.patch_size
							snake_case_          =						(
							    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:
														snake_case_          =						AutoImageProcessor.from_pretrained(model_args.image_processor_name			,	**_SCREAMING_SNAKE_CASE			)
							elif model_args.model_name_or_path:
														snake_case_          =						AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,	**_SCREAMING_SNAKE_CASE			)
							else:
														snake_case_          =						{
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														snake_case_          =						IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														snake_case_          =						AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path			,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)			,	config=_SCREAMING_SNAKE_CASE			,	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"""			)
														snake_case_          =						AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE			)

							if training_args.do_train:
														snake_case_          =						ds["""train"""].column_names
							else:
														snake_case_          =						ds["""validation"""].column_names

							if data_args.image_column_name is not None:
														snake_case_          =						data_args.image_column_name
							elif "image" in column_names:
														snake_case_          =						"""image"""
							elif "img" in column_names:
														snake_case_          =						"""img"""
							else:
														snake_case_          =						column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							snake_case_          =						Compose(
							    [
							        Lambda(lambda _SCREAMING_SNAKE_CASE			: 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
							snake_case_          =						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(_SCREAMING_SNAKE_CASE			):
														snake_case_          =						[transforms(_SCREAMING_SNAKE_CASE			) for image in examples[image_column_name]]
														snake_case_          =						[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:
																					snake_case_          =						ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
														# Set the training transforms
														ds["train"].set_transform(_SCREAMING_SNAKE_CASE			)

							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:
																					snake_case_          =						(
																					    ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
																					)
														# Set the validation transforms
														ds["validation"].set_transform(_SCREAMING_SNAKE_CASE			)

							# Initialize our trainer
							snake_case_          =						Trainer(
							    model=_SCREAMING_SNAKE_CASE			,	args=_SCREAMING_SNAKE_CASE			,	train_dataset=ds["""train"""] if training_args.do_train else None			,	eval_dataset=ds["""validation"""] if training_args.do_eval else None			,	tokenizer=_SCREAMING_SNAKE_CASE			,	data_collator=_SCREAMING_SNAKE_CASE			,	)

							# Training
							if training_args.do_train:
														snake_case_          =						None
														if training_args.resume_from_checkpoint is not None:
																					snake_case_          =						training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					snake_case_          =						last_checkpoint
														snake_case_          =						trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE			)
														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:
														snake_case_          =						trainer.evaluate()
														trainer.log_metrics("""eval"""			,	_SCREAMING_SNAKE_CASE			)
														trainer.save_metrics("""eval"""			,	_SCREAMING_SNAKE_CASE			)

							# Write model card and (optionally) push to hub
							snake_case_          =						{
							    """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(**_SCREAMING_SNAKE_CASE			)
							else:
														trainer.create_model_card(**_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				main()







"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			int:
							snake_case_          =						[]
							for i in range(config.num_hidden_layers			):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""cls_token""", """vit.embeddings.cls_token"""),
							        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]			)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]			)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														snake_case_          =						[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""			) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]			)

							return rename_keys







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			List[Any]:
							for i in range(config.num_hidden_layers			):
														if base_model:
																					snake_case_          =						""""""
														else:
																					snake_case_          =						"""vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
														# next, add query, keys and values (in that order) to the state dict
														snake_case_          =						in_proj_weight[
														    : config.hidden_size, :
														]
														snake_case_          =						in_proj_bias[: config.hidden_size]
														snake_case_          =						in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														snake_case_          =						in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														snake_case_          =						in_proj_weight[
														    -config.hidden_size :, :
														]
														snake_case_          =						in_proj_bias[-config.hidden_size :]







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						dct.pop(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						val







def   _a			( )      ->			Dict:
							snake_case_          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,	stream=_SCREAMING_SNAKE_CASE			).raw			)
							return im







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=True			)      ->			Any:
							snake_case_          =						ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														snake_case_          =						8
							# set labels if required
							if not base_model:
														snake_case_          =						1_000
														snake_case_          =						"""huggingface/label-files"""
														snake_case_          =						"""imagenet-1k-id2label.json"""
														snake_case_          =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	repo_type="""dataset"""			)			,	"""r"""			)			)
														snake_case_          =						{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
														snake_case_          =						idalabel
														snake_case_          =						{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														snake_case_          =						384
														snake_case_          =						1_536
														snake_case_          =						12
														snake_case_          =						6

							# load original model from torch hub
							snake_case_          =						torch.hub.load("""facebookresearch/dino:main"""			,	_SCREAMING_SNAKE_CASE			)
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							snake_case_          =						original_model.state_dict()
							if base_model:
														remove_classification_head_(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						create_rename_keys(_SCREAMING_SNAKE_CASE			,	base_model=_SCREAMING_SNAKE_CASE			)
							for src, dest in rename_keys:
														rename_key(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							read_in_q_k_v(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# load HuggingFace model
							if base_model:
														snake_case_          =						ViTModel(_SCREAMING_SNAKE_CASE			,	add_pooling_layer=_SCREAMING_SNAKE_CASE			).eval()
							else:
														snake_case_          =						ViTForImageClassification(_SCREAMING_SNAKE_CASE			).eval()
							model.load_state_dict(_SCREAMING_SNAKE_CASE			)

							# Check outputs on an image, prepared by ViTImageProcessor
							snake_case_          =						ViTImageProcessor()
							snake_case_          =						image_processor(images=prepare_img()			,	return_tensors="""pt"""			)
							snake_case_          =						encoding["""pixel_values"""]
							snake_case_          =						model(_SCREAMING_SNAKE_CASE			)

							if base_model:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.last_hidden_state[:, 0, :]			,	atol=1E-1			)
							else:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.logits			,	atol=1E-3			)

							Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
							print(f"""Saving model {model_name} 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 __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='dino_vitb16',
				    type=str,
				    help='Name of the model trained with DINO you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--base_model',
				    action='store_true',
				    help='Whether to only convert the base model (no projection head weights).',
				)

				parser.set_defaults(base_model=True)
				__SCREAMING_SNAKE_CASE							:      int						=      parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



