

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







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: Optional[str]       			=			field(
				    default='''cifar10'''	,  metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
				__SCREAMING_SNAKE_CASE					: Optional[str]       			=			field(
				    default=lowercase_	,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
				__SCREAMING_SNAKE_CASE					: Optional[str]       			=			field(
				    default=lowercase_	,  metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}	,  )
				__SCREAMING_SNAKE_CASE					: Optional[str]       			=			field(default=lowercase_	,  metadata={'''help''': '''A folder containing the training data.'''}			)
				__SCREAMING_SNAKE_CASE					: Optional[str]       			=			field(default=lowercase_	,  metadata={'''help''': '''A folder containing the validation data.'''}			)
				__SCREAMING_SNAKE_CASE					: Optional[float]       			=			field(
				    default=0.15	,  metadata={'''help''': '''Percent to split off of train for validation.'''}			)
				__SCREAMING_SNAKE_CASE					: int       			=			field(default=32	,  metadata={'''help''': '''The size of the square patches to use for masking.'''}			)
				__SCREAMING_SNAKE_CASE					: float       			=			field(
				    default=0.6	,  metadata={'''help''': '''Percentage of patches to mask.'''}	,  )
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			field(
				    default=lowercase_	,  metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }	,  )
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			field(
				    default=lowercase_	,  metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }	,  )




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







class  lowercase				:




				def __init__(					self      ,   snake_case=192      ,   snake_case=32      ,   snake_case=4      ,   snake_case=0.6       ):
											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       ):
											snake_case_							=    np.random.permutation(self.token_count       )[: self.mask_count]
											snake_case_							=    np.zeros(self.token_count      ,   dtype=snake_case       )
											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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							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 					__lowerCamelCase	( ):







							'''simple docstring'''






							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'   ,							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()

							snake_case_							=    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.
							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   ,							UpperCamelCase__      ) 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   ,							**UpperCamelCase__      )
							elif model_args.model_name_or_path:
														snake_case_							=    AutoConfig.from_pretrained(model_args.model_name_or_path   ,							**UpperCamelCase__      )
							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(UpperCamelCase__   ,							'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   ,							**UpperCamelCase__      )
							elif model_args.model_name_or_path:
														snake_case_							=    AutoImageProcessor.from_pretrained(model_args.model_name_or_path   ,							**UpperCamelCase__      )
							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=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'      )
														snake_case_							=    AutoModelForMaskedImageModeling.from_config(UpperCamelCase__      )

							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 UpperCamelCase__      : 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(UpperCamelCase__      ):
														snake_case_							=    [transforms(UpperCamelCase__      ) 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(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:
																					snake_case_							=    (
																					    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
							snake_case_							=    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:
														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=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:
														snake_case_							=    trainer.evaluate()
														trainer.log_metrics('eval'   ,							UpperCamelCase__      )
														trainer.save_metrics('eval'   ,							UpperCamelCase__      )

							# 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(**UpperCamelCase__      )
							else:
														trainer.create_model_card(**UpperCamelCase__      )


if __name__ == "__main__":
					main()







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




				@property
				def     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

											if len(snake_case       ) != len(snake_case       ):
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(snake_case       )} operations while'''
																		    F''' destination module has {len(snake_case       )}.'''       )

											for dest_m, src_m in zip(snake_case      ,   snake_case       ):
																		dest_m.load_state_dict(src_m.state_dict()       )
																		if self.verbose == 1:
																									print(F'''Transfered from={src_m} to={dest_m}'''       )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

							assert torch.allclose(from_model(UpperCamelCase__      )   ,							our_model(UpperCamelCase__      ).logits      ), "The model logits don't match the original one."

							snake_case_							=    F'''resnet{"-".join(name.split("resnet"      )      )}'''
							print(UpperCamelCase__      )

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add model'   ,							use_temp_dir=UpperCamelCase__   ,							)

														# we can use the convnext one
														snake_case_							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add image processor'   ,							use_temp_dir=UpperCamelCase__   ,							)

														print(F'''Pushed {checkpoint_name}'''      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							snake_case_							=    'imagenet-1k-id2label.json'
							snake_case_							=    1000
							snake_case_							=    (1, num_labels)

							snake_case_							=    'huggingface/label-files'
							snake_case_							=    num_labels
							snake_case_							=    json.load(open(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

							snake_case_							=    idalabel
							snake_case_							=    {v: k for k, v in idalabel.items()}

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							snake_case_							=    {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							}

							if model_name:
														convert_weight_and_push(UpperCamelCase__   ,							names_to_config[model_name]   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							return config, expected_shape


if __name__ == "__main__":
					_UpperCAmelCase		:   List[str]										=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
					        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					_UpperCAmelCase		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   Path										=       args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




