





'''simple docstring'''

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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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   	=    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")







@dataclass
class __a			:
   __UpperCamelCase :			Optional[str]						   =						field(
       default='cifar10', metadata={'help': 'Name of a dataset from the datasets package'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={'help': 'The column name of the images in the files.'}      )
   __UpperCamelCase :			Optional[str]						   =						field(default=__SCREAMING_SNAKE_CASE, metadata={'help': 'A folder containing the training data.'}      )
   __UpperCamelCase :			Optional[str]						   =						field(default=__SCREAMING_SNAKE_CASE, metadata={'help': 'A folder containing the validation data.'}      )
   __UpperCamelCase :			Optional[float]						   =						field(
       default=0.15, metadata={'help': 'Percent to split off of train for validation.'}      )
   __UpperCamelCase :			Optional[int]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }, )
   __UpperCamelCase :			Optional[int]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }, )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __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 __a			:
   __UpperCamelCase :			str						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={
           'help': (
               'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={
           'help': (
               'Override some existing default config settings when a model is trained from scratch. Example: '
               'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}      )
   __UpperCamelCase :			str						   =						field(
       default='main', metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}, )
   __UpperCamelCase :			str						   =						field(default=__SCREAMING_SNAKE_CASE, metadata={'help': 'Name or path of preprocessor config.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       }, )
   __UpperCamelCase :			float						   =						field(
       default=0.75, metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=__SCREAMING_SNAKE_CASE, metadata={'help': 'Whether or not to train with normalized pixel values as target.'}      )







@dataclass
class __a			( __SCREAMING_SNAKE_CASE      ):
   __UpperCamelCase :			float						   =						field(
       default=1E-3, metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}      )





def     __magic_name__							(				__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.stack([example["""pixel_values"""] for example in examples]   )
   return {"pixel_values": pixel_values}

def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
   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_mae"""				,						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"""]

   # Load pretrained model and image processor
   #
   # 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:
      __SCREAMING_SNAKE_CASE   				=							ViTMAEConfig.from_pretrained(model_args.config_name				,						**lowerCAmelCase_   )
   elif model_args.model_name_or_path:
      __SCREAMING_SNAKE_CASE   				=							ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,						**lowerCAmelCase_   )
   else:
      __SCREAMING_SNAKE_CASE   				=							ViTMAEConfig()
      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}"""   )

    # adapt config
   config.update(
       {
           """mask_ratio""": model_args.mask_ratio,
           """norm_pix_loss""": model_args.norm_pix_loss,
       }   )

   # create image processor
   if model_args.image_processor_name:
      __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor.from_pretrained(model_args.image_processor_name				,						**lowerCAmelCase_   )
   elif model_args.model_name_or_path:
      __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,						**lowerCAmelCase_   )
   else:
      __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      __SCREAMING_SNAKE_CASE   				=							ViTMAEForPreTraining.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   				=							ViTMAEForPreTraining(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 MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      __SCREAMING_SNAKE_CASE   				=							image_processor.size["""shortest_edge"""]
   else:
      __SCREAMING_SNAKE_CASE   				=							(image_processor.size["""height"""], image_processor.size["""width"""])
   __SCREAMING_SNAKE_CASE   				=							Compose(
       [
           Lambda(lambda __UpperCAmelCase   : img.convert("""RGB"""   ) if img.mode != "RGB" else img   ),
           RandomResizedCrop(lowerCAmelCase_				,						scale=(0.2, 1.0)				,						interpolation=InterpolationMode.BICUBIC   ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean				,						std=image_processor.image_std   ),
       ]   )

   def preprocess_images(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							[transforms(lowerCAmelCase_   ) for image in 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_   )

   # Compute absolute learning rate
   __SCREAMING_SNAKE_CASE   				=							(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      __SCREAMING_SNAKE_CASE   				=							training_args.base_learning_rate * total_train_batch_size / 256

   # 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   				=							{
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**lowerCAmelCase_   )
   else:
      trainer.create_model_card(**lowerCAmelCase_   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   main()


if __name__ == "__main__":
    main()












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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A					:				str				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[1_4_4, 1_9_2, 2_4_0]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
    elif "mobilevit_xs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[9_6, 1_2_0, 1_4_4]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
    elif "mobilevit_xxs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[6_4, 8_0, 9_6]
        SCREAMING_SNAKE_CASE__											=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
        SCREAMING_SNAKE_CASE__											=			0.05
        SCREAMING_SNAKE_CASE__											=			2.0

    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			5_1_2
        SCREAMING_SNAKE_CASE__											=			1_6
        SCREAMING_SNAKE_CASE__											=			2_1
        SCREAMING_SNAKE_CASE__											=			'''pascal-voc-id2label.json'''
    else:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''

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

    return config


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    for i in range(1 ,							6	):
        if f'''layer_{i}.''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''layer_{i}.''' ,							f'''encoder.layer.{i - 1}.'''	)

    if "conv_1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''conv_1.''' ,							'''conv_stem.'''	)
    if ".block." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.block.''' ,							'''.'''	)

    if "exp_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''exp_1x1''' ,							'''expand_1x1'''	)
    if "red_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''red_1x1''' ,							'''reduce_1x1'''	)
    if ".local_rep.conv_3x3." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_3x3.''' ,							'''.conv_kxk.'''	)
    if ".local_rep.conv_1x1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_1x1.''' ,							'''.conv_1x1.'''	)
    if ".norm." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.norm.''' ,							'''.normalization.'''	)
    if ".conv." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv.''' ,							'''.convolution.'''	)
    if ".conv_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv_proj.''' ,							'''.conv_projection.'''	)

    for i in range(0 ,							2	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.layer.{j}.'''	)

    for i in range(2 ,							6	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.'''	)
                if "expand_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''expand_1x1''' ,							'''downsampling_layer.expand_1x1'''	)
                if "conv_3x3" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''conv_3x3''' ,							'''downsampling_layer.conv_3x3'''	)
                if "reduce_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''reduce_1x1''' ,							'''downsampling_layer.reduce_1x1'''	)

    for i in range(2 ,							5	):
        if f'''.global_rep.{i}.weight''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.weight''' ,							'''.layernorm.weight'''	)
        if f'''.global_rep.{i}.bias''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.bias''' ,							'''.layernorm.bias'''	)

    if ".global_rep." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.global_rep.''' ,							'''.transformer.'''	)
    if ".pre_norm_mha.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.0.''' ,							'''.layernorm_before.'''	)
    if ".pre_norm_mha.1.out_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.1.out_proj.''' ,							'''.attention.output.dense.'''	)
    if ".pre_norm_ffn.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.0.''' ,							'''.layernorm_after.'''	)
    if ".pre_norm_ffn.1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.1.''' ,							'''.intermediate.dense.'''	)
    if ".pre_norm_ffn.4." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.4.''' ,							'''.output.dense.'''	)
    if ".transformer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.transformer.''' ,							'''.transformer.layer.'''	)

    if ".aspp_layer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_layer.''' ,							'''.'''	)
    if ".aspp_pool." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_pool.''' ,							'''.'''	)
    if "seg_head." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''seg_head.''' ,							'''segmentation_head.'''	)
    if "segmentation_head.classifier.classifier." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''segmentation_head.classifier.classifier.''' ,							'''segmentation_head.classifier.'''	)

    if "classifier.fc." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''classifier.fc.''' ,							'''classifier.'''	)
    elif (not base_model) and ("segmentation_head." not in name):
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.''' + name

    return name


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Optional[Any]:
    if base_model:
        SCREAMING_SNAKE_CASE__											=			''''''
    else:
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__											=			orig_state_dict.pop(lowerCAmelCase_	)

        if key[:8] == "encoder.":
            SCREAMING_SNAKE_CASE__											=			key[8:]

        if "qkv" in key:
            SCREAMING_SNAKE_CASE__											=			key.split('''.'''	)
            SCREAMING_SNAKE_CASE__											=			int(key_split[0][6:]	) - 1
            SCREAMING_SNAKE_CASE__											=			int(key_split[3]	)
            SCREAMING_SNAKE_CASE__											=			model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''	)
            SCREAMING_SNAKE_CASE__											=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
            SCREAMING_SNAKE_CASE__											=			(
                f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                SCREAMING_SNAKE_CASE__											=			val[:dim, :]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE__											=			val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE__											=			val[:dim]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2]
                SCREAMING_SNAKE_CASE__											=			val[-dim:]
        else:
            SCREAMING_SNAKE_CASE__											=			val

    return orig_state_dict


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			get_mobilevit_config(lowerCAmelCase_	)

    # load original state_dict
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location='''cpu'''	)

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			MobileViTForSemanticSegmentation(lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			MobileViTForImageClassification(lowerCAmelCase_	).eval()

    SCREAMING_SNAKE_CASE__											=			convert_state_dict(lowerCAmelCase_ ,							lowerCAmelCase_	)
    model.load_state_dict(lowerCAmelCase_	)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    SCREAMING_SNAKE_CASE__											=			MobileViTImageProcessor(crop_size=config.image_size ,							size=config.image_size + 3_2	)
    SCREAMING_SNAKE_CASE__											=			image_processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			outputs.logits

    if mobilevit_name.startswith('''deeplabv3_'''	):
        assert logits.shape == (1, 2_1, 3_2, 3_2)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                    [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                    [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                    [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                    [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                    [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                    [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                ]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)
    else:
        assert logits.shape == (1, 1_0_0_0)

        if mobilevit_name == "mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-0.98_66, 0.23_92, -1.12_41]	)
        elif mobilevit_name == "mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-2.47_61, -0.93_99, -1.95_87]	)
        elif mobilevit_name == "mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-1.93_64, -1.23_27, -0.46_53]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(lowerCAmelCase_	)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
    image_processor.save_pretrained(lowerCAmelCase_	)

    if push_to_hub:
        SCREAMING_SNAKE_CASE__											=			{
            '''mobilevit_s''': '''mobilevit-small''',
            '''mobilevit_xs''': '''mobilevit-x-small''',
            '''mobilevit_xxs''': '''mobilevit-xx-small''',
            '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
            '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
            '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
        }

        print('''Pushing to the hub...'''	)
        SCREAMING_SNAKE_CASE__											=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)
        model.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)


if __name__ == "__main__":
  _A					:				str				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--mobilevit_name""",
      default="""mobilevit_s""",
      type=str,
      help=(
          """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
          """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
      ),
  )
  parser.add_argument(
      """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _A					:				str				=			parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
