




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
lowerCamelCase__					:  str      							=				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')

lowerCamelCase__					:  Any      							=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase__					:  Optional[int]      							=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}
       if self.train_dir is not None:
             SCREAMING_SNAKE_CASE_      		=     self.train_dir
       if self.validation_dir is not None:
             SCREAMING_SNAKE_CASE_      		=     self.validation_dir
       SCREAMING_SNAKE_CASE_      		=     data_files if data_files else None





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







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





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Dict=192							,							_lowerCAmelCase    :						Any=32							,							_lowerCAmelCase    :						str=4							,							_lowerCAmelCase    :						Union[str, Any]=0.6		):
       SCREAMING_SNAKE_CASE_      		=     input_size
       SCREAMING_SNAKE_CASE_      		=     mask_patch_size
       SCREAMING_SNAKE_CASE_      		=     model_patch_size
       SCREAMING_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'		)

       SCREAMING_SNAKE_CASE_      		=     self.input_size // self.mask_patch_size
       SCREAMING_SNAKE_CASE_      		=     self.mask_patch_size // self.model_patch_size

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




 def __call__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     np.random.permutation(self.token_count		)[: self.mask_count]
       SCREAMING_SNAKE_CASE_      		=     np.zeros(self.token_count							,							dtype=__snake_case		)
       SCREAMING_SNAKE_CASE_      		=     1

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

       return torch.tensor(mask.flatten()		)


def    UpperCAmelCase_				( __UpperCAmelCase		:      Union[str, Any]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     torch.stack([example['pixel_values'] for example in examples]				)
      SCREAMING_SNAKE_CASE_      		=     torch.stack([example['mask'] for example in examples]				)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def    UpperCAmelCase_				( )   ->					Union[str, Any]:
      # 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.

      SCREAMING_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.
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_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'     ,     __lowerCAmelCase     ,     __lowerCAmelCase				)

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

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

      SCREAMING_SNAKE_CASE_      		=     training_args.get_process_log_level()
      logger.setLevel(__lowerCAmelCase				)
      transformers.utils.logging.set_verbosity(__lowerCAmelCase				)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

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

      # Detecting last checkpoint.
      SCREAMING_SNAKE_CASE_      		=     None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_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.
      SCREAMING_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.
      SCREAMING_SNAKE_CASE_      		=     None if 'validation' in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split     ,     __lowerCAmelCase				) and data_args.train_val_split > 0.0:
            SCREAMING_SNAKE_CASE_      		=     ds['train'].train_test_split(data_args.train_val_split				)
            SCREAMING_SNAKE_CASE_      		=     split['train']
            SCREAMING_SNAKE_CASE_      		=     split['test']

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      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,
      }
      if model_args.config_name_or_path:
            SCREAMING_SNAKE_CASE_      		=     AutoConfig.from_pretrained(model_args.config_name_or_path     ,     **__lowerCAmelCase				)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE_      		=     AutoConfig.from_pretrained(model_args.model_name_or_path     ,     **__lowerCAmelCase				)
      else:
            SCREAMING_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(__lowerCAmelCase     ,     'decoder_type'				):
            SCREAMING_SNAKE_CASE_      		=     'simmim'

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

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

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

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

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

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

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

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE_      		=     None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_      		=     training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_      		=     last_checkpoint
            SCREAMING_SNAKE_CASE_      		=     trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
            trainer.save_model()
            trainer.log_metrics('train'     ,     train_result.metrics				)
            trainer.save_metrics('train'     ,     train_result.metrics				)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            SCREAMING_SNAKE_CASE_      		=     trainer.evaluate()
            trainer.log_metrics('eval'     ,     __lowerCAmelCase				)
            trainer.save_metrics('eval'     ,     __lowerCAmelCase				)

      # Write model card and (optionally) push to hub
      SCREAMING_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(**__lowerCAmelCase				)
      else:
            trainer.create_model_card(**__lowerCAmelCase				)


if __name__ == "__main__":
   main()






'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

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

   snake_case 						=   name
   print(__lowerCAmelCase		)

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name		)
      snake_case 						=   LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name		)

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

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

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

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[2, 3, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 8, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]      ,    num_attention_heads=[3, 5, 6]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]      ,    num_attention_heads=[6, 9, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0.1      ,    ),
   }

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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 Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			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)

